diff --git a/synthetic/README.md b/synthetic/README.md
index ea4fec3..86114be 100644
--- a/synthetic/README.md
+++ b/synthetic/README.md
@@ -42,8 +42,16 @@ which slots nest, because the model alone does not decide that: BDCHM gives
 as a uuid5 string while `value_quantity` is nested inline.
 
 It also reports where the model and the data disagree on cardinality. That is
-not hypothetical — BDCHM declares `identity` multivalued and every
-transformation spec, RTI's included, emits a scalar.
+not hypothetical — BDCHM declares `identity` and `Condition.associated_evidence`
+multivalued, and every transformation spec, RTI's included, emits a scalar for
+both.
+
+The `associated_evidence` case has a sharp edge worth knowing about: in
+linkml-map a `value:` derivation emits a scalar, while `populated_from` with
+`value_mappings` honours the multivalued declaration and emits a list. Using
+both for one slot leaves it carrying two shapes, and `schema.py` types a column
+from the first record it sees — so it would get the rest wrong. The specs here
+branch with `case()` instead, which stays scalar and matches what RTI emits.
 
 Then, from a dm-bip checkout:
 
@@ -83,6 +91,10 @@ without running the pipeline. Regenerate it with `python sample.py` after a run.
 | Race | 50/30/10/10 white, black, Asian, American Indian | 60/20/10/10 white, black, Middle Eastern, Native Hawaiian |
 | BMI | continuous | categorical |
 
+Both carry the same five conditions — heart failure, family history of stroke,
+hypertension, Type 2 diabetes, and myocardial infarction — and the same
+measurements, including fasting glucose and HbA1c.
+
 5% of Study Two's participants are the same individuals as in Study One. They
 share a `dbGaP_Subject_ID`, so harmonization resolves them to one `Person` with
 two `Participant` records — the same way real cross-study participation appears.
@@ -90,6 +102,68 @@ two `Participant` records — the same way real cross-study participation appear
 One visit per participant is `TELEHEALTH`; the rest are `STUDY_SITE_VISIT`.
 Deceased participants stop attending, so nobody is measured after they die.
 
+## Time
+
+Conditions carry `age_at_condition_start`, `age_at_condition_end` where they
+resolved, and the `associated_visit` that recorded them. Measurements carry
+`age_at_observation` and their own `associated_visit` — including the systolic
+and diastolic observations nested inside a blood pressure set, which are
+queryable on their own and should not need a join back through the set to be
+placed in time.
+
+The reason to populate those slots is that a quarter of diagnoses are **incident**:
+made between two visits rather than before enrolment. A condition only makes
+the measurements it explains abnormal from its diagnosis onwards, and stops
+once it resolves, so the same participant's results fall into two distinct
+distributions:
+
+```
+glucose at or above 126 mg/dL, participants diagnosed with T2D mid-study
+  before the diagnosis   10.3%     (the corpus-wide rate)
+  after  the diagnosis   52.2%     (the diabetic rate)
+```
+
+A corpus where every diagnosis predates every visit answers "measurements after
+diagnosis" with the whole cohort, and the temporal slots are decoration.
+`validate.py` checks the separation rather than merely checking the slots are
+non-null.
+
+Family history is the exception: `age_at_condition_start` is defined as the
+*participant's* age, which means nothing for a condition a parent had, so
+family-history records carry the recording visit and no ages.
+
+## Concepts and source terminology
+
+Hypertension and Type 2 diabetes are coded from the BDC cohort-readiness
+code-set reference — 13 hypertension subtypes and 11 for T2D, against the
+single `HP:0000822` and no diabetes at all that the corpus started with. A
+participant screened and found negative is coded to the hierarchy root, since a
+questionnaire asks about hypertension in general; a subtype is recorded once
+there is a diagnosis.
+
+SNOMED CT and ICD-10-CM appear **only in the raw dbGaP-style tables**. BDCHM has
+no slot for source terminology anywhere — `condition_concept` is single-valued
+over `ConditionConceptEnum`, which is MONDO ∪ HPO — so mapping source codes into
+harmonized output would mean inventing a place to put them. Leaving them in the
+raw layer is also what real data looks like, and it means the transformation
+exercises the code-mapping path rather than assuming it away.
+
+That matters most for T2D, where the reference gives `MONDO:0005148` for nearly
+every row and puts the complication-level detail in ICD-10-CM. The granularity
+this corpus offers for diabetes complications lives in the source codes.
+
+Two reference rows are deliberately not used, and one class of cell is not
+guessed at:
+
+- **Pregnancy-related hypertension** — both cohorts enrol at 45-78.
+- **T2D with ophthalmic complications** — its ICD-10-CM cell is the bare
+  wildcard `E11.3*` and its MONDO is the shared root, so it would be
+  indistinguishable from every other T2D record.
+- Cells naming a hierarchy rather than a term (`"44054006 hierarchy + renal
+  complication descendants"`) or a wildcard rather than a code become `None`
+  rather than a guess. A subtype with no concrete source code emits none, which
+  is a shape real data has too.
+
 ## Known shapes that surprise people
 
 **`cause_of_death` is present for living participants**, multivalued, with a null
@@ -124,7 +198,9 @@ has to render:
 - `associated_assay` referencing a CBC instance (WBC)
 - `qualifier` marking a value as an average (BUN)
 - `Condition.relationship_to_participant` for family history
-- `associated_evidence` on study-record-sourced conditions
+- `associated_evidence` distinguishing an ECG-backed infarction from self-report
+- `age_at_condition_start` / `_end` and `associated_visit` on conditions
+- `age_at_observation` on every measurement, nested ones included
 - `exposure_status` distinguishing absent from present drug exposures
 - Continuous and categorical presentations of the same concept across cohorts
 
diff --git a/synthetic/generate.py b/synthetic/generate.py
index 0131ac2..ee0c136 100644
--- a/synthetic/generate.py
+++ b/synthetic/generate.py
@@ -26,29 +26,58 @@
         7,
     ),
     "visit": (["dbGaP_Subject_ID", "SUBJECT_ID", "VISIT_NUM", "VISIT_TYPE", "AGE_DAYS"], 4),
+    # AGE_DAYS repeats the visit's age on every measurement table. It is
+    # derivable by joining to the visit table, but real dbGaP measurement tables
+    # carry the age alongside the result, and it is what age_at_observation is
+    # populated from without requiring the transformation to join.
     "clinical": (
-        ["dbGaP_Subject_ID", "SUBJECT_ID", "VISIT_NUM", "HEIGHT_CM", "WEIGHT_KG", "BMI", "SBP", "DBP"],
-        7,
+        ["dbGaP_Subject_ID", "SUBJECT_ID", "VISIT_NUM", "AGE_DAYS",
+         "HEIGHT_CM", "WEIGHT_KG", "BMI", "SBP", "DBP"],
+        8,
     ),
     "labs": (
-        ["dbGaP_Subject_ID", "SUBJECT_ID", "VISIT_NUM", "HDL", "HDL_OPERATOR", "BUN", "WBC"],
-        6,
+        ["dbGaP_Subject_ID", "SUBJECT_ID", "VISIT_NUM", "AGE_DAYS",
+         "HDL", "HDL_OPERATOR", "BUN", "WBC", "GLUCOSE", "HBA1C"],
+        9,
     ),
+    # One row per participant, so the visit that recorded each condition is a
+    # per-condition column rather than the table-level VISIT_NUM the measurement
+    # tables carry. SNOMED and ICD-10-CM appear only here: BDCHM has no slot for
+    # source terminology, so they stay in the raw layer, which is where they sit
+    # in real dbGaP tables too.
     "conditions": (
         [
             "dbGaP_Subject_ID",
             "SUBJECT_ID",
             "HEART_FAILURE",
             "HF_CONCEPT",
+            "HF_AGE_START",
+            "HF_VISIT",
             "FAM_STROKE",
             "FS_CONCEPT",
             "FS_RELATIVE",
+            "FS_VISIT",
             "HYPERTENSION",
+            "HTN_CONCEPT",
+            "HTN_SNOMED",
+            "HTN_ICD10",
+            "HTN_AGE_START",
+            "HTN_AGE_END",
+            "HTN_VISIT",
+            "DIABETES",
+            "DM_CONCEPT",
+            "DM_SNOMED",
+            "DM_ICD10",
+            "DM_AGE_START",
+            "DM_AGE_END",
+            "DM_VISIT",
             "HEART_ATTACK",
             "HA_CONCEPT",
             "HA_SOURCE",
+            "HA_AGE_START",
+            "HA_VISIT",
         ],
-        10,
+        28,
     ),
     "meds": (["dbGaP_Subject_ID", "SUBJECT_ID", "CCB_STATUS", "CCB_CONCEPT"], 3),
 }
@@ -98,25 +127,33 @@ def build_rows(participants, study):
         for visit in p.visits:
             rows["visit"].append([gid, sid, visit.number, visit.category, visit.age_days])
             rows["clinical"].append([
-                gid, sid, visit.number,
+                gid, sid, visit.number, visit.age_days,
                 visit.height_cm, visit.weight_kg,
                 visit.bmi_category if study.bmi_categorical else visit.bmi,
                 visit.systolic, visit.diastolic,
             ])
             rows["labs"].append([
-                gid, sid, visit.number,
+                gid, sid, visit.number, visit.age_days,
                 visit.hdl, visit.hdl_operator, visit.bun, visit.wbc,
+                visit.glucose, visit.hba1c,
             ])
 
         c = p.conditions
+        hf, fs, htn, dm, ha = (
+            c["heart_failure"], c["family_stroke"], c["hypertension"],
+            c["diabetes"], c["heart_attack"],
+        )
         rows["conditions"].append([
             gid, sid,
-            c["heart_failure"]["status"], c["heart_failure"]["concept"],
-            c["family_stroke"]["status"], c["family_stroke"]["concept"],
-            c["family_stroke"]["relationship"],
-            c["hypertension"]["status"],
-            c["heart_attack"]["status"], c["heart_attack"]["concept"],
-            "STUDY_RECORD" if c["heart_attack"]["from_study_record"] else "SELF_REPORT",
+            hf["status"], hf["concept"], hf["age_start"], hf["visit"],
+            fs["status"], fs["concept"], fs["relationship"], fs["visit"],
+            htn["status"], htn["concept"], htn["snomed"], htn["icd10"],
+            htn["age_start"], htn["age_end"], htn["visit"],
+            dm["status"], dm["concept"], dm["snomed"], dm["icd10"],
+            dm["age_start"], dm["age_end"], dm["visit"],
+            ha["status"], ha["concept"],
+            "STUDY_RECORD" if ha["from_study_record"] else "SELF_REPORT",
+            ha["age_start"], ha["visit"],
         ])
 
         rows["meds"].append([
diff --git a/synthetic/population.py b/synthetic/population.py
index 5a4826d..06853a6 100644
--- a/synthetic/population.py
+++ b/synthetic/population.py
@@ -29,10 +29,22 @@
     "heart_failure": ("systolic", "diastolic", "bmi", "hdl", "bun"),
     "hypertension": ("systolic", "diastolic"),
     "heart_attack": ("systolic", "hdl"),
+    "diabetes": ("glucose", "hba1c", "bmi"),
 }
 # Exactly this many HDL results fall below the assay's lower limit.
 HDL_BELOW_LLOD = 5
 
+# A quarter of the conditions a participant ever has are diagnosed during
+# follow-up rather than before enrolment. Without incident cases there is
+# nothing for a measurement to fall before or after, and the temporal slots
+# carry no query value — a corpus where every diagnosis predates every visit
+# answers 'measurements after diagnosis' with the whole cohort.
+INCIDENT_FRACTION = 0.25
+# A prevalent condition starts this many years before the first visit.
+PREVALENT_YEARS = (5, 20)
+# Nobody is diagnosed before this age, however far back the draw reaches.
+MIN_DIAGNOSIS_AGE_DAYS = 18 * 365
+
 HDL_LLOD = 5.0
 HDL_ULOD = 150.0
 
@@ -135,6 +147,8 @@ class Visit:
     hdl_operator: str | None = None
     bun: float | None = None
     wbc: float | None = None
+    glucose: float | None = None
+    hba1c: float | None = None
 
 
 def _weighted(rng, mapping):
@@ -152,15 +166,28 @@ def _measure(rng, normal, pathological, rate=PATHOLOGY_RATE):
     return round(rng.uniform(lo, hi), 1)
 
 
-def _elevated(conditions):
-    """Which measures this participant's conditions make abnormal more often."""
+def _elevated(conditions, age_days):
+    """
+    Which measures this participant's conditions make abnormal at this age.
+
+    A condition only elevates measurements taken while it is active: at or
+    after diagnosis, and not after it resolved. That is what makes 'glucose
+    before diagnosis' a different distribution from 'glucose after it', rather
+    than the same elevated draw applied to every visit a participant attended.
+    """
     if not conditions:
         return frozenset()
     measures = set()
     for name, linked in COMORBIDITY.items():
         entry = conditions.get(name)
-        if entry and entry["status"] in (v.PRESENT, v.HISTORICAL):
-            measures.update(linked)
+        if not entry or entry["status"] not in (v.PRESENT, v.HISTORICAL):
+            continue
+        start, end = entry["age_start"], entry["age_end"]
+        if start is None or age_days < start:
+            continue
+        if end is not None and age_days > end:
+            continue
+        measures.update(linked)
     return frozenset(measures)
 
 
@@ -210,7 +237,7 @@ def _make_visits(rng, study, person):
 
 
 def _fill_measurements(rng, study, visit, conditions=None):
-    elevated = _elevated(conditions)
+    elevated = _elevated(conditions, visit.age_days)
 
     def rate(measure):
         return COMORBID_PATHOLOGY_RATE if measure in elevated else PATHOLOGY_RATE
@@ -236,17 +263,87 @@ def rate(measure):
     visit.hdl = _maybe_null(rng, _measure(rng, (40, 72), (22, 39), rate("hdl")))
     visit.bun = _maybe_null(rng, _measure(rng, (7, 20), (21, 46), rate("bun")))
     visit.wbc = _maybe_null(rng, _measure(rng, (4.5, 11.0), (1.8, 19.5), rate("wbc")))
+    # Fasting glucose and HbA1c step straight from normal to diabetic; the
+    # corpus does not model the prediabetic band between them, in keeping with
+    # every other measure here being drawn from one range or the other.
+    visit.glucose = _maybe_null(rng, _measure(rng, (70, 99), (126, 260), rate("glucose")))
+    visit.hba1c = _maybe_null(rng, _measure(rng, (4.8, 5.6), (6.5, 11.5), rate("hba1c")))
+
+
+def _diagnosis(rng, visits, resolves=False):
+    """
+    Date a diagnosis: when it started, when it ended, and which visit recorded it.
+
+    Prevalent conditions start before enrolment and are recorded at the first
+    visit — the common case, and the only one the corpus had before. Incident
+    ones start between two visits and are recorded at the next, which is what
+    puts some of a participant's measurements before the diagnosis and some
+    after.
+
+    Ages are in days, matching BDCHM's age_at_condition_start and _end.
+    """
+    if not visits:
+        return None, None, None
+
+    if len(visits) > 1 and rng.random() < INCIDENT_FRACTION:
+        i = rng.randrange(1, len(visits))
+        start = rng.randint(visits[i - 1].age_days + 1, visits[i].age_days)
+        recorded = visits[i]
+    else:
+        lo, hi = PREVALENT_YEARS
+        start = visits[0].age_days - rng.randint(lo * 365, hi * 365)
+        start = max(start, MIN_DIAGNOSIS_AGE_DAYS)
+        recorded = visits[0]
 
+    # A resolved condition ends somewhere between diagnosis and the last visit
+    # the participant attended, so nothing resolves after they stop being seen.
+    end = None
+    if resolves and start < visits[-1].age_days:
+        end = rng.randint(start + 1, visits[-1].age_days)
+    return start, end, recorded.number
 
-def _make_conditions(rng):
-    """Condition statuses follow the proportions in the brief."""
+
+def _absent(concept, subtype=None):
+    """
+    Build a condition that was asked about and answered no.
+
+    No diagnosis means no dates and no source code: a screening question
+    records the concept it screened for, not a coded diagnosis.
+    """
+    entry = {
+        "status": v.ABSENT,
+        "concept": concept,
+        "age_start": None,
+        "age_end": None,
+        "visit": None,
+    }
+    if subtype is not None:
+        entry.update(snomed=None, icd10=None, subtype=subtype.label)
+    return entry
+
+
+def _make_conditions(rng, visits):
+    """
+    Condition statuses follow the proportions in the brief.
+
+    Each condition the participant has carries the temporal metadata BDCHM
+    provides for it — age at start, age at end where it resolved, and the visit
+    that recorded it. Absent conditions carry none of the three.
+    """
     conditions = {}
 
     r = rng.random()
-    conditions["heart_failure"] = {
-        "status": v.PRESENT if r < 0.15 else v.ABSENT,
-        "concept": rng.choice(v.HEART_FAILURE),
-    }
+    if r < 0.15:
+        start, end, visit = _diagnosis(rng, visits)
+        conditions["heart_failure"] = {
+            "status": v.PRESENT,
+            "concept": rng.choice(v.HEART_FAILURE),
+            "age_start": start,
+            "age_end": end,
+            "visit": visit,
+        }
+    else:
+        conditions["heart_failure"] = _absent(rng.choice(v.HEART_FAILURE))
 
     r = rng.random()
     if r < 0.35:
@@ -255,30 +352,102 @@ def _make_conditions(rng):
         status = v.ABSENT
     else:
         status = v.UNKNOWN
+    # Family history is about a relative, so age_at_condition_start — defined as
+    # the *participant's* age — has no meaning here. The visit that recorded the
+    # answer does, and is populated.
     conditions["family_stroke"] = {
         "status": status,
         "concept": rng.choice(v.STROKE),
         "relationship": rng.choice([v.NATURAL_FATHER, v.NATURAL_MOTHER]),
+        "age_start": None,
+        "age_end": None,
+        "visit": visits[0].number if visits else None,
     }
 
+    conditions["hypertension"] = _make_hypertension(rng, visits)
+    conditions["diabetes"] = _make_diabetes(rng, visits)
+
+    if rng.random() < 0.10:
+        start, _, visit = _diagnosis(rng, visits)
+        conditions["heart_attack"] = {
+            "status": v.PRESENT,
+            "concept": rng.choice(v.HEART_ATTACK),
+            # Half of those with an MI have it from the study record, with an
+            # ECG in evidence; the rest self-report.
+            "from_study_record": rng.random() < 0.5,
+            "age_start": start,
+            # An infarction is an event, not a state that resolves, so it has a
+            # start and no end.
+            "age_end": None,
+            "visit": visit,
+        }
+    else:
+        entry = _absent(rng.choice(v.HEART_ATTACK))
+        entry["from_study_record"] = False
+        conditions["heart_attack"] = entry
+
+    return conditions
+
+
+def _make_hypertension(rng, visits):
+    """
+    Hypertension, coded to a subtype from the code-set reference when diagnosed.
+
+    HISTORICAL means resolved, so those carry an age_at_condition_end.
+    """
     r = rng.random()
     if r < 0.10:
         status = v.PRESENT
     elif r < 0.20:
         status = v.HISTORICAL
     else:
-        status = v.ABSENT
-    conditions["hypertension"] = {"status": status, "concept": v.HYPERTENSION}
-
-    present = rng.random() < 0.10
-    conditions["heart_attack"] = {
-        "status": v.PRESENT if present else v.ABSENT,
-        "concept": rng.choice(v.HEART_ATTACK),
-        # Half of those with an MI have it from the study record, with an ECG
-        # in evidence; the rest self-report.
-        "from_study_record": present and rng.random() < 0.5,
+        return _absent(v.HYPERTENSION_ROOT.concept, v.HYPERTENSION_ROOT)
+
+    subtype = _weighted(rng, {s: s.weight for s in v.HYPERTENSION_SUBTYPES})
+    start, end, visit = _diagnosis(rng, visits, resolves=status == v.HISTORICAL)
+    return {
+        "status": status,
+        "concept": subtype.concept,
+        "subtype": subtype.label,
+        "snomed": subtype.snomed,
+        "icd10": rng.choice(subtype.icd10) if subtype.icd10 else None,
+        "age_start": start,
+        "age_end": end,
+        "visit": visit,
+    }
+
+
+def _make_diabetes(rng, visits):
+    """
+    Type 2 diabetes, coded to a subtype from the code-set reference.
+
+    The reference gives MONDO:0005148 for nearly every T2D row and puts the
+    complication detail in ICD-10-CM, so the concept is often the same across
+    subtypes and the source code is what distinguishes them. 'In remission' is
+    the one subtype that resolves, and is recorded HISTORICAL with an end age.
+    """
+    r = rng.random()
+    if r < 0.02:
+        entry = _absent(v.DIABETES_ROOT.concept, v.DIABETES_ROOT)
+        entry["status"] = v.UNKNOWN
+        return entry
+    if r >= 0.22:
+        return _absent(v.DIABETES_ROOT.concept, v.DIABETES_ROOT)
+
+    subtype = _weighted(rng, {s: s.weight for s in v.DIABETES_SUBTYPES})
+    remission = subtype is v.DIABETES_REMISSION
+    status = v.HISTORICAL if remission else v.PRESENT
+    start, end, visit = _diagnosis(rng, visits, resolves=remission)
+    return {
+        "status": status,
+        "concept": subtype.concept,
+        "subtype": subtype.label,
+        "snomed": subtype.snomed,
+        "icd10": rng.choice(subtype.icd10) if subtype.icd10 else None,
+        "age_start": start,
+        "age_end": end,
+        "visit": visit,
     }
-    return conditions
 
 
 def build():
@@ -315,11 +484,13 @@ def build():
             )
             next_subject[study.name] += 1
 
-            # Conditions first: measurements are drawn conditional on them, so
-            # that an abnormal result and the diagnosis explaining it co-occur.
-            participant.conditions = _make_conditions(rng)
-
+            # Visits, then conditions, then measurements. Conditions are dated
+            # against the visit schedule, and measurements are drawn conditional
+            # on which conditions were active at the visit — so an abnormal
+            # result and the diagnosis explaining it co-occur, and only from the
+            # diagnosis onwards.
             participant.visits = _make_visits(rng, study, person)
+            participant.conditions = _make_conditions(rng, participant.visits)
             for visit in participant.visits:
                 _fill_measurements(rng, study, visit, participant.conditions)
 
diff --git a/synthetic/sample.py b/synthetic/sample.py
index 2e5f70d..16ef304 100644
--- a/synthetic/sample.py
+++ b/synthetic/sample.py
@@ -56,11 +56,41 @@
         ("BMI as a category",
          lambda d: d.get("observation_type") == "OMOP:3038553"
          and d.get("value_quantity", {}).get("value_concept") is not None),
+        ("fasting glucose, anchored to an age and a visit",
+         lambda d: d.get("observation_type") == "OMOP:4156660"
+         and d.get("age_at_observation") is not None),
+        ("HbA1c, reported as a percentage",
+         lambda d: d.get("observation_type") == "OMOP:4184637"
+         and d.get("value_quantity", {}).get("unit") == "%"),
     ],
     "Condition": [
         ("family history, with the relative recorded",
          lambda d: d.get("relationship_to_participant", "").startswith("OMOP:")),
         ("about oneself", lambda d: d.get("relationship_to_participant") == "ONESELF"),
+        # The temporal slots, which are what make a condition queryable against
+        # the measurements taken around it.
+        ("ongoing, dated to the age at diagnosis",
+         lambda d: d.get("age_at_condition_start") and not d.get("age_at_condition_end")),
+        ("resolved, with both a start and an end age",
+         lambda d: d.get("age_at_condition_start") and d.get("age_at_condition_end")),
+        # Scoped to diabetes rather than any absent condition, so the record
+        # shown is the screening answer the neighbouring T2D entry is the
+        # diagnosed counterpart to.
+        ("undiagnosed: screened for, so no dates and no source code",
+         lambda d: d.get("condition_concept") == "MONDO:0005148"
+         and d.get("condition_status") == "ABSENT"),
+        # Concept breadth: T2D and a hypertension subtype more specific than the
+        # hierarchy root everyone else is coded to. Both must be diagnosed —
+        # MONDO:0005148 is also what a negative diabetes screen is coded to, so
+        # matching on the concept alone would label a participant who does not
+        # have diabetes 'Type 2 diabetes'.
+        ("Type 2 diabetes",
+         lambda d: d.get("condition_concept") == "MONDO:0005148"
+         and d.get("condition_status") in ("PRESENT", "HISTORICAL")),
+        ("hypertensive heart disease, not just 'hypertension'",
+         lambda d: d.get("condition_concept") == "MONDO:0001302"),
+        ("an infarction with an ECG in evidence rather than self-report",
+         lambda d: d.get("associated_evidence") == "electrocardiogram"),
     ],
     "DrugExposure": [
         ("taking a calcium channel blocker", lambda d: d.get("drug_concept")),
@@ -76,13 +106,23 @@ def load(path):
 
 
 def pick(records, selectors):
-    """Take the first record matching each selector, keeping the labels."""
-    chosen = []
+    """
+    Take the first unused record matching each selector, keeping the labels.
+
+    Selectors overlap — a record can be about oneself, undiagnosed, and coded
+    to diabetes all at once — so without skipping what earlier selectors took,
+    one record turns up several times under several labels and the sample stops
+    showing as many distinct shapes as it claims to.
+    """
+    chosen, seen = [], set()
     for label, predicate in selectors:
         for record in records:
+            if record.get("id") in seen:
+                continue
             try:
                 if predicate(record):
                     chosen.append((label, record))
+                    seen.add(record.get("id"))
                     break
             except (AttributeError, TypeError):
                 continue
diff --git a/synthetic/sample/Condition.yaml b/synthetic/sample/Condition.yaml
index 0faa180..4f253bc 100644
--- a/synthetic/sample/Condition.yaml
+++ b/synthetic/sample/Condition.yaml
@@ -1,31 +1,185 @@
 # study_one: family history, with the relative recorded
+associated_evidence: self-report questionnaire
 associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
-condition_concept: MONDO:0005264
+associated_visit: 75122f58-efd2-559d-9eac-2619199795aa
+condition_concept: MONDO:0013792
 condition_provenance: PATIENT_SELF-REPORTED_CONDITION
 condition_status: ABSENT
 id: 1bacbc56-4aaf-5017-b70c-42fa03f9d48b
 relationship_to_participant: OMOP:4321888
 ---
 # study_one: about oneself
+age_at_condition_end: null
+age_at_condition_start: null
+associated_evidence: self-report questionnaire
 associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
-condition_concept: MONDO:0005068
+associated_visit: null
+condition_concept: MONDO:0005148
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: ABSENT
+id: 93dc8788-7f85-5c52-b78f-0244bd7f1f3b
+relationship_to_participant: ONESELF
+---
+# study_one: ongoing, dated to the age at diagnosis
+age_at_condition_end: null
+age_at_condition_start: 22043
+associated_evidence: self-report questionnaire
+associated_participant: 1e3b97bf-f17d-53ab-b05b-da66139cf65a
+associated_visit: 91a85e60-c7b1-55f6-9c7a-1caef7e28fc1
+condition_concept: MONDO:0005148
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: PRESENT
+id: fe9fd9e2-68c7-541e-a276-f5410ab6c5ae
+relationship_to_participant: ONESELF
+---
+# study_one: resolved, with both a start and an end age
+age_at_condition_end: 23679
+age_at_condition_start: 20465
+associated_evidence: self-report questionnaire
+associated_participant: 005f1434-9203-5c31-8d4f-4426ea0d1edc
+associated_visit: 912a37e2-3cd7-5e43-90e4-ea31243a63cc
+condition_concept: MONDO:0005148
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: HISTORICAL
+id: 2e1fc958-7841-5bb9-ab91-051f7d35e061
+relationship_to_participant: ONESELF
+---
+# study_one: undiagnosed: screened for, so no dates and no source code
+age_at_condition_end: null
+age_at_condition_start: null
+associated_evidence: self-report questionnaire
+associated_participant: e805802b-da55-5d52-9db3-810ad3ed2058
+associated_visit: null
+condition_concept: MONDO:0005148
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: ABSENT
+id: 7421193c-9e57-5018-ad8e-956ccb1df1a5
+relationship_to_participant: ONESELF
+---
+# study_one: Type 2 diabetes
+age_at_condition_end: null
+age_at_condition_start: 17887
+associated_evidence: self-report questionnaire
+associated_participant: 94470960-2d15-5ae5-8eb7-664b56478e73
+associated_visit: d3e958b4-898c-5942-be79-765f4890d4f6
+condition_concept: MONDO:0005148
 condition_provenance: PATIENT_SELF-REPORTED_CONDITION
 condition_status: PRESENT
-id: 4058ed2d-92d4-5b8a-af0e-aee32c2c9960
+id: 696a19ca-77f3-5443-af49-36d06f7a33ca
+relationship_to_participant: ONESELF
+---
+# study_one: hypertensive heart disease, not just 'hypertension'
+age_at_condition_end: null
+age_at_condition_start: 22145
+associated_evidence: self-report questionnaire
+associated_participant: 4bf678da-a746-5d29-a100-2814a8973b28
+associated_visit: 8c37d465-a2ae-5a97-b0fa-6ffc0e0625ec
+condition_concept: MONDO:0001302
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: PRESENT
+id: d4033a16-0e38-522b-99a3-a7f7d21bbc53
+relationship_to_participant: ONESELF
+---
+# study_one: an infarction with an ECG in evidence rather than self-report
+age_at_condition_start: 15441
+associated_evidence: electrocardiogram
+associated_participant: c033e47e-9147-55f9-a2f2-d9aee07f19d9
+associated_visit: 35c0c7fc-fb59-567f-903f-cac28727e381
+condition_concept: MONDO:0006803
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: PRESENT
+id: 4b55221e-27aa-5882-9b26-485714d8a2c9
 relationship_to_participant: ONESELF
 ---
 # study_two: family history, with the relative recorded
+associated_evidence: self-report questionnaire
 associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
-condition_concept: MONDO:0013792
+associated_visit: 22bf721d-ce80-5f08-b1c5-d19857b89b15
+condition_concept: MONDO:0006809
 condition_provenance: PATIENT_SELF-REPORTED_CONDITION
-condition_status: UNKNOWN
+condition_status: ABSENT
 id: 87bc5b14-516d-598a-a33d-0bd3d7b92615
 relationship_to_participant: OMOP:4321888
 ---
 # study_two: about oneself
+age_at_condition_end: null
+age_at_condition_start: null
+associated_evidence: self-report questionnaire
 associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
-condition_concept: MONDO:0005068
+associated_visit: null
+condition_concept: MONDO:0005148
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: ABSENT
+id: ac60fd9e-c1ff-55b9-829b-261592dbaaf5
+relationship_to_participant: ONESELF
+---
+# study_two: ongoing, dated to the age at diagnosis
+age_at_condition_end: null
+age_at_condition_start: 16623
+associated_evidence: self-report questionnaire
+associated_participant: 95a9eb19-e6ee-59bd-bb86-1a9f46b34601
+associated_visit: 1f8fd2ab-290a-551e-9f62-14dc76f5d60a
+condition_concept: MONDO:0005148
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: PRESENT
+id: cd68a5aa-db88-5dfa-9e87-86cc123056be
+relationship_to_participant: ONESELF
+---
+# study_two: resolved, with both a start and an end age
+age_at_condition_end: 26131
+age_at_condition_start: 25125
+associated_evidence: self-report questionnaire
+associated_participant: 16f1434c-7148-5af2-bf4b-3c9b0f5345bd
+associated_visit: 1e7adf97-1060-5efd-be0b-af4b2b0357d8
+condition_concept: MONDO:0005148
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: HISTORICAL
+id: dfd1a68f-e83b-5bbe-b087-0fa6986fa01f
+relationship_to_participant: ONESELF
+---
+# study_two: undiagnosed: screened for, so no dates and no source code
+age_at_condition_end: null
+age_at_condition_start: null
+associated_evidence: self-report questionnaire
+associated_participant: fd79135a-f146-5bd8-8774-cd330887be23
+associated_visit: null
+condition_concept: MONDO:0005148
 condition_provenance: PATIENT_SELF-REPORTED_CONDITION
 condition_status: ABSENT
-id: 67332483-bbf9-57fd-98de-9f54e47bcd9e
+id: 359d1efa-1663-55eb-ae26-49e37732b615
+relationship_to_participant: ONESELF
+---
+# study_two: Type 2 diabetes
+age_at_condition_end: null
+age_at_condition_start: 25238
+associated_evidence: self-report questionnaire
+associated_participant: e562d1f8-aea8-5991-b75f-5f135d78015f
+associated_visit: f4071f3b-98cf-5adf-9e62-ee3ed69111c6
+condition_concept: MONDO:0005148
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: PRESENT
+id: 1abb7978-2941-5bfd-ae1a-f3ad8f1716ef
+relationship_to_participant: ONESELF
+---
+# study_two: hypertensive heart disease, not just 'hypertension'
+age_at_condition_end: 27128
+age_at_condition_start: 20216
+associated_evidence: self-report questionnaire
+associated_participant: 6d489c3a-0277-5196-8034-a232e2b76e3a
+associated_visit: 6c3f6f5d-788d-5eda-a287-e6f7598e9d95
+condition_concept: MONDO:0001302
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: HISTORICAL
+id: 93201bc3-a85a-5db5-ac2e-7f0a02d48eb3
+relationship_to_participant: ONESELF
+---
+# study_two: an infarction with an ECG in evidence rather than self-report
+age_at_condition_start: 19714
+associated_evidence: electrocardiogram
+associated_participant: cd0524eb-56c3-53f2-bd42-3a8aa2cceb36
+associated_visit: c8edc65c-3eb1-5a90-a683-b28aee2660e6
+condition_concept: MONDO:0005068
+condition_provenance: PATIENT_SELF-REPORTED_CONDITION
+condition_status: PRESENT
+id: daf67a3e-8982-50fe-ba22-aba5987760d3
 relationship_to_participant: ONESELF
diff --git a/synthetic/sample/Demography.yaml b/synthetic/sample/Demography.yaml
index b9c45d5..ebf8a71 100644
--- a/synthetic/sample/Demography.yaml
+++ b/synthetic/sample/Demography.yaml
@@ -1,10 +1,10 @@
 # study_one: Hispanic ethnicity
-associated_participant: dd37092b-1515-5c8b-aebf-18507b1f98f6
+associated_participant: 0875292e-8eee-572d-9876-7a3d49bb7e6a
 ethnicity: OMOP:38003563
-id: 27107a6a-5d26-54ac-90cd-c243d4102d3a
+id: ca8665d8-d9d6-567e-a492-5430c33bc9f3
 race:
-- OMOP:8527
-sex: OMOP:8507
+- OMOP:8516
+sex: OMOP:8532
 ---
 # study_one: non-Hispanic
 associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
@@ -15,11 +15,11 @@ race:
 sex: OMOP:8507
 ---
 # study_two: Hispanic ethnicity
-associated_participant: 390f76f9-c667-5487-a1b9-ecd8b5fe9927
+associated_participant: 8c4e8af1-e644-58f4-8dc4-0c3bba8a3df0
 ethnicity: OMOP:38003563
-id: 021e4f47-6504-5725-b1e0-d79450ce064a
+id: 90b255f1-7ac0-5283-b31e-9d0574f5c263
 race:
-- OMOP:8527
+- OMOP:8516
 sex: OMOP:8532
 ---
 # study_two: non-Hispanic
@@ -27,5 +27,5 @@ associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
 ethnicity: OMOP:38003564
 id: 18e9e092-8ad5-5414-b9e8-677ff7d886d1
 race:
-- OMOP:8515
-sex: OMOP:8532
+- OMOP:8527
+sex: OMOP:8507
diff --git a/synthetic/sample/DrugExposure.yaml b/synthetic/sample/DrugExposure.yaml
index 9ec6e0b..95c42b2 100644
--- a/synthetic/sample/DrugExposure.yaml
+++ b/synthetic/sample/DrugExposure.yaml
@@ -13,11 +13,11 @@ exposure_status: ABSENT
 id: f9592e79-ad0d-5e45-b130-a5823ecc447a
 ---
 # study_two: taking a calcium channel blocker
-associated_participant: 0a0a6844-cd9d-5401-95b0-f7dfa5d910db
-drug_concept: NDFRT:N0000175421
+associated_participant: 49cc38d7-99f6-5d03-ba66-901c6bf3fa4a
+drug_concept: RxCUI:7417
 exposure_provenance: PATIENT_SELF_REPORTED_MEDICATION
 exposure_status: PRESENT
-id: 5cd32ad9-48ef-570f-a1bb-c7ef61bbc57c
+id: 06fb143b-6517-5844-90e4-9e68f8f85281
 ---
 # study_two: not taking one
 associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
diff --git a/synthetic/sample/MeasurementObservation.yaml b/synthetic/sample/MeasurementObservation.yaml
index 8d8a406..4c941eb 100644
--- a/synthetic/sample/MeasurementObservation.yaml
+++ b/synthetic/sample/MeasurementObservation.yaml
@@ -1,4 +1,5 @@
 # study_one: HDL below the assay's lower limit, carrying the operator
+age_at_observation: 20075
 associated_assay:
   id: ddcb07cc-0d54-5828-93a8-495a3f8a3f07
   lower_limit_of_detection:
@@ -10,17 +11,18 @@ associated_assay:
     id: b0655aa0-8a6e-5719-809d-e5678af1e9e6
     unit: mg/dL
     value_decimal: 150.0
-associated_participant: 9e9b7cbd-07b5-5ea9-9912-6cc95a1e8ca5
-associated_visit: 7b35a86e-1813-56e3-97d2-0fbb66803db0
-id: b01f532f-d4cd-5dc7-b494-79b5876a6e8c
+associated_participant: 161a3327-cd8f-5d53-b3fd-1e0d94063646
+associated_visit: 8cfdc8e5-8d5d-561e-8f30-800564338c57
+id: 5d27f1d2-8527-5998-9b9d-ec0f2520cd74
 observation_type: OMOP:3007070
 value_quantity:
-  id: be2ab2d1-121b-547f-aae9-c647ddf3d69f
+  id: be0ac0b6-6f27-5a00-842d-133d8cb0d990
   operator: <
   unit: mg/dL
   value_decimal: 5.0
 ---
 # study_one: HDL with assay detection limits
+age_at_observation: 27375
 associated_assay:
   id: ddcb07cc-0d54-5828-93a8-495a3f8a3f07
   lower_limit_of_detection:
@@ -40,9 +42,10 @@ value_quantity:
   id: 16ec110d-a004-5269-a4f0-33574ed63357
   operator: null
   unit: mg/dL
-  value_decimal: 58.4
+  value_decimal: 45.1
 ---
 # study_one: WBC referencing the CBC assay
+age_at_observation: 27375
 associated_assay:
   id: 25bf7403-bd81-5764-9e90-8af5246333ff
   method: MMO:0000533
@@ -53,9 +56,10 @@ observation_type: OMOP:3000905
 value_quantity:
   id: 071da59d-a293-55d0-a8ab-141a15d99ab3
   unit: 10*3/uL
-  value_decimal: 10.4
+  value_decimal: 6.0
 ---
 # study_one: BUN qualified as an average
+age_at_observation: 27375
 associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
 associated_visit: 75122f58-efd2-559d-9eac-2619199795aa
 id: 74e323bb-6fa2-5a03-b8d4-01d1b37713bb
@@ -64,9 +68,10 @@ qualifier: AVERAGE
 value_quantity:
   id: 54f1a4f3-26f9-532c-af11-0f9a032fcc7e
   unit: mg/dL
-  value_decimal: 18.4
+  value_decimal: 8.1
 ---
 # study_one: BMI as a continuous value
+age_at_observation: 27375
 associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
 associated_visit: 75122f58-efd2-559d-9eac-2619199795aa
 id: dcfddca5-aea6-55ea-a3f5-05c8ea5e87b6
@@ -74,9 +79,34 @@ observation_type: OMOP:3038553
 value_quantity:
   id: 69a41584-68c4-5f4c-ab51-419b2f2416f7
   unit: kg/m2
-  value_decimal: 21.9
+  value_decimal: 22.2
+---
+# study_one: fasting glucose, anchored to an age and a visit
+age_at_observation: 27375
+associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
+associated_visit: 75122f58-efd2-559d-9eac-2619199795aa
+id: f0660717-b1ad-5ee2-971f-dafe2acb6f1f
+method_type: CALIBRATED
+observation_type: OMOP:4156660
+value_quantity:
+  id: 8a984fc0-87d8-5de3-9273-85a9e3a40853
+  unit: mg/dL
+  value_decimal: 147.3
+---
+# study_one: HbA1c, reported as a percentage
+age_at_observation: 27375
+associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
+associated_visit: 75122f58-efd2-559d-9eac-2619199795aa
+id: 9d71fb65-1fa1-5b33-ba61-e3e178121ed1
+method_type: blood assay
+observation_type: OMOP:4184637
+value_quantity:
+  id: e446c271-5140-5a09-836f-c4517ab63d46
+  unit: '%'
+  value_decimal: null
 ---
 # study_two: HDL below the assay's lower limit, carrying the operator
+age_at_observation: 17155
 associated_assay:
   id: 1533b5fa-b7d7-5d34-a075-88950c88b9b3
   lower_limit_of_detection:
@@ -88,17 +118,18 @@ associated_assay:
     id: cd40e959-ee21-55fb-bcd2-a6a84fdae679
     unit: mg/dL
     value_decimal: 150.0
-associated_participant: f82b92bc-cc81-5d6d-9fcc-e2b1df68b733
-associated_visit: ca601664-8b89-5304-8f58-200d2ccaa4a2
-id: e5e4b790-b5ed-57ee-8679-9c75b4af3960
+associated_participant: 69772f79-909d-5669-bcde-83fa2132b25d
+associated_visit: c8f93751-d44c-5b98-9276-94a6eb2bf682
+id: 7dc60dd9-677a-5926-a045-9a458dbc4e87
 observation_type: OMOP:3007070
 value_quantity:
-  id: 31829a46-4891-541c-b0ae-437badfdb600
+  id: 14c0713c-1c86-56e2-b011-093e6b9c80d2
   operator: <
   unit: mg/dL
   value_decimal: 5.0
 ---
 # study_two: HDL with assay detection limits
+age_at_observation: 28470
 associated_assay:
   id: 1533b5fa-b7d7-5d34-a075-88950c88b9b3
   lower_limit_of_detection:
@@ -118,9 +149,10 @@ value_quantity:
   id: 9c071689-64ca-58df-b23b-1c664ee4336f
   operator: null
   unit: mg/dL
-  value_decimal: 63.7
+  value_decimal: 51.2
 ---
 # study_two: WBC referencing the CBC assay
+age_at_observation: 28470
 associated_assay:
   id: c1a9ff74-d562-5bd5-b97f-15a2ed7a8e57
   method: MMO:0000533
@@ -131,9 +163,10 @@ observation_type: OMOP:3000905
 value_quantity:
   id: a91f6e17-efb3-5168-b09b-0a66df0abe43
   unit: 10*3/uL
-  value_decimal: 9.9
+  value_decimal: 8.2
 ---
 # study_two: BUN qualified as an average
+age_at_observation: 28470
 associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
 associated_visit: 22bf721d-ce80-5f08-b1c5-d19857b89b15
 id: d5c1a7cf-457f-5274-b8f5-41644eb4b1c5
@@ -142,9 +175,10 @@ qualifier: AVERAGE
 value_quantity:
   id: 6e3ab74d-99e2-5419-984a-29361d647941
   unit: mg/dL
-  value_decimal: 17.9
+  value_decimal: 13.8
 ---
 # study_two: BMI as a category
+age_at_observation: 28470
 associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
 associated_visit: 22bf721d-ce80-5f08-b1c5-d19857b89b15
 id: 6b2572f5-a244-504c-9f4f-7dae5a26ceb0
@@ -152,3 +186,27 @@ observation_type: OMOP:3038553
 value_quantity:
   id: f427eb8f-c038-5c83-9ea3-ffa4586a6728
   value_concept: normal weight
+---
+# study_two: fasting glucose, anchored to an age and a visit
+age_at_observation: 28470
+associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
+associated_visit: 22bf721d-ce80-5f08-b1c5-d19857b89b15
+id: 5a62b826-238f-59ea-964e-798aa9a21311
+method_type: CALIBRATED
+observation_type: OMOP:4156660
+value_quantity:
+  id: 1caaceec-be4a-51fa-85c0-9bf51827e9b3
+  unit: mg/dL
+  value_decimal: 73.5
+---
+# study_two: HbA1c, reported as a percentage
+age_at_observation: 28470
+associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
+associated_visit: 22bf721d-ce80-5f08-b1c5-d19857b89b15
+id: 53448361-7e8f-509e-9da6-2686214541d5
+method_type: blood assay
+observation_type: OMOP:4184637
+value_quantity:
+  id: ce0c92b9-08a7-5b36-bd9b-f2a617efd1dd
+  unit: '%'
+  value_decimal: 5.4
diff --git a/synthetic/sample/MeasurementObservationSet.yaml b/synthetic/sample/MeasurementObservationSet.yaml
index 15b032b..1184281 100644
--- a/synthetic/sample/MeasurementObservationSet.yaml
+++ b/synthetic/sample/MeasurementObservationSet.yaml
@@ -3,7 +3,9 @@ associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
 associated_visit: 75122f58-efd2-559d-9eac-2619199795aa
 id: f443b266-2d72-5429-8df4-43dfaf1bcc12
 observations:
-- associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
+- age_at_observation: 27375
+  associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
+  associated_visit: 75122f58-efd2-559d-9eac-2619199795aa
   body_position: SITTING_POSITION
   body_site: Right arm
   id: daae1547-43eb-5db9-88a0-1fc442022951
@@ -11,8 +13,10 @@ observations:
   value_quantity:
     id: 7fe894d1-919d-573a-a17d-d36adc78f486
     unit: mm[Hg]
-    value_decimal: 164.3
-- associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
+    value_decimal: 128.7
+- age_at_observation: 27375
+  associated_participant: ded1d538-866f-5c18-8aa2-0b24f1f08a5a
+  associated_visit: 75122f58-efd2-559d-9eac-2619199795aa
   body_position: SITTING_POSITION
   body_site: Right arm
   id: ecee49c5-c3c5-540b-b723-f43b78f05656
@@ -20,14 +24,16 @@ observations:
   value_quantity:
     id: f2c228df-56c8-56a0-946d-d8c06f69d7e0
     unit: mm[Hg]
-    value_decimal: 82.6
+    value_decimal: 82.5
 ---
 # study_two: blood pressure, systolic and diastolic in one set
 associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
 associated_visit: 22bf721d-ce80-5f08-b1c5-d19857b89b15
 id: e9806428-dd0d-5ea0-b9d4-a7b21d8156c5
 observations:
-- associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
+- age_at_observation: 28470
+  associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
+  associated_visit: 22bf721d-ce80-5f08-b1c5-d19857b89b15
   body_position: SITTING_POSITION
   body_site: Right arm
   id: 2d648e6e-ea07-5879-a4f8-569930bc20d0
@@ -35,8 +41,10 @@ observations:
   value_quantity:
     id: d7463d3b-6bbb-53a5-9ee8-d61c723822bd
     unit: mm[Hg]
-    value_decimal: 128.2
-- associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
+    value_decimal: 124.8
+- age_at_observation: 28470
+  associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
+  associated_visit: 22bf721d-ce80-5f08-b1c5-d19857b89b15
   body_position: SITTING_POSITION
   body_site: Right arm
   id: f3d69c77-bf43-5e06-9185-ce6cac66e8de
@@ -44,4 +52,4 @@ observations:
   value_quantity:
     id: cbe14372-7714-55f0-a567-db4b1735f728
     unit: mm[Hg]
-    value_decimal: 94.6
+    value_decimal: 72.0
diff --git a/synthetic/sample/Participant.yaml b/synthetic/sample/Participant.yaml
index 97a6123..9882bd2 100644
--- a/synthetic/sample/Participant.yaml
+++ b/synthetic/sample/Participant.yaml
@@ -5,7 +5,7 @@ identity: '100001'
 member_of_research_study: Example Study One
 ---
 # study_two: enrolment, linked to a Person
-associated_person: b9733cac-ed81-52b8-bb35-ac8f1fccb21f
+associated_person: 383327ec-dd83-5a40-a8a5-07aa34bd31d6
 id: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
 identity: '200001'
 member_of_research_study: Example Study Two
diff --git a/synthetic/sample/Person.yaml b/synthetic/sample/Person.yaml
index f547a0a..0c47024 100644
--- a/synthetic/sample/Person.yaml
+++ b/synthetic/sample/Person.yaml
@@ -20,20 +20,20 @@ vital_status: OMOP:4230556
 ---
 # study_two: deceased, with cause of death
 cause_of_death:
-- cause: ICD10CM:R99
-  id: c810c2ab-9b85-58f1-a7ba-009eccd3b843
+- cause: ICD10CM:I20-I25
+  id: dec11e22-1e3d-5caa-8f2e-431632c49f26
   order: 1
-id: 79153ada-4607-5077-a30d-93cc240aa7df
-identity: '900441'
+id: 5dcbaad9-f657-5cfe-b8f3-c180aaf0fc56
+identity: '900449'
 species: NCBITaxon:9606
 vital_status: OMOP:434489
 ---
 # study_two: living, note the empty cause_of_death entry
 cause_of_death:
 - cause: null
-  id: c92da1ee-347b-585a-a12c-bda32ec3c201
+  id: 77455809-ed44-58e7-8916-5fb170defbf4
   order: null
-id: b9733cac-ed81-52b8-bb35-ac8f1fccb21f
-identity: '900093'
+id: 383327ec-dd83-5a40-a8a5-07aa34bd31d6
+identity: '900483'
 species: NCBITaxon:9606
 vital_status: OMOP:4230556
diff --git a/synthetic/sample/Visit.yaml b/synthetic/sample/Visit.yaml
index b8797ee..83f195d 100644
--- a/synthetic/sample/Visit.yaml
+++ b/synthetic/sample/Visit.yaml
@@ -13,15 +13,15 @@ visit_category: STUDY_SITE_VISIT
 visit_provenance: CASE_REPORT_FORM
 ---
 # study_two: telehealth
-age_at_visit_start: 22630
+age_at_visit_start: 28470
 associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
-id: 17f3fa62-5837-571d-9e3a-5b089b4c1d9d
+id: 22bf721d-ce80-5f08-b1c5-d19857b89b15
 visit_category: TELEHEALTH
 visit_provenance: CASE_REPORT_FORM
 ---
 # study_two: study site visit
-age_at_visit_start: 21170
+age_at_visit_start: 29200
 associated_participant: e9d16f51-a014-5b89-bb71-d3550b9a7cbb
-id: 22bf721d-ce80-5f08-b1c5-d19857b89b15
+id: 3e539a4f-bd85-5be1-a262-e65e539ba5e9
 visit_category: STUDY_SITE_VISIT
 visit_provenance: CASE_REPORT_FORM
diff --git a/synthetic/specs.py b/synthetic/specs.py
index ec83de1..488772b 100644
--- a/synthetic/specs.py
+++ b/synthetic/specs.py
@@ -54,13 +54,34 @@ def ident(study, table):
     return subject, f"'uuid5(\"{PARTICIPANT_NS}\", str({{{subject}}}) + \":{study.name}\")'"
 
 
-def visit_ref(study, table):
-    """Build the uuid5 reference to a row's visit."""
+def visit_ref(study, table, column="VISIT_NUM"):
+    """
+    Build the uuid5 reference to a row's visit.
+
+    The measurement tables carry one VISIT_NUM for the row. The conditions
+    table is one row per participant, so each condition names the visit that
+    recorded it in its own column.
+    """
     subject = phv(study, table, "SUBJECT_ID")
-    num = phv(study, table, "VISIT_NUM")
+    num = phv(study, table, column)
     return f"'uuid5(\"{VISIT_NS}\", str({{{subject}}}) + \":{study.name} VISIT \" + str({{{num}}}))'"
 
 
+def observed_at(study, table):
+    """
+    Build the two temporal anchors every MeasurementObservation should carry.
+
+    RTI derives age_at_observation as '{age_in_years} * 365' because their raw
+    columns hold years; ours hold days already, so this is a plain
+    populated_from and nulls propagate rather than needing a case() guard.
+    """
+    return (
+        f"        associated_visit:\n          expr: {visit_ref(study, table)}\n"
+        f"        age_at_observation:\n"
+        f"          populated_from: {phv(study, table, 'AGE_DAYS')}\n"
+    )
+
+
 def person_and_participant(study):
     """Build the Person and Participant derivations."""
     t = study.tables["subject"]
@@ -157,6 +178,9 @@ def blood_pressure(study):
     dbp = phv(study, "clinical", "DBP")
 
     def observation(concept, source, label):
+        # The set carries associated_visit too, but an observation is also
+        # queryable on its own, and a consumer filtering MeasurementObservation
+        # by time should not have to join back through the set to do it.
         return f"""          - MeasurementObservation:
               populated_from: {t}
               slot_derivations:
@@ -164,6 +188,10 @@ def observation(concept, source, label):
                   expr: {uid(OBSERVATION_NS, row_key(study, "clinical", label))}
                 associated_participant:
                   expr: {participant}
+                associated_visit:
+                  expr: {visit}
+                age_at_observation:
+                  populated_from: {phv(study, "clinical", "AGE_DAYS")}
                 observation_type:
                   value: {concept}
                 body_position:
@@ -208,7 +236,6 @@ def simple_measure(study, table, column, concept, unit, extra="", categorical=Fa
     """
     t = study.tables[table]
     _, participant = ident(study, table)
-    visit = visit_ref(study, table)
     key = row_key(study, table, column)
     if categorical:
         value_slot = "value_concept"
@@ -224,9 +251,7 @@ def simple_measure(study, table, column, concept, unit, extra="", categorical=Fa
           expr: {uid(OBSERVATION_NS, key)}
         associated_participant:
           expr: {participant}
-        associated_visit:
-          expr: {visit}
-        observation_type:
+{observed_at(study, table)}        observation_type:
           value: {concept}
 {extra}        value_quantity:
           class_derivations:
@@ -245,7 +270,6 @@ def hdl(study):
     t = study.tables["labs"]
     operator = phv(study, "labs", "HDL_OPERATOR")
     _, participant = ident(study, "labs")
-    visit = visit_ref(study, "labs")
     return f"""- class_derivations:
     MeasurementObservation:
       populated_from: {t}
@@ -254,9 +278,7 @@ def hdl(study):
           expr: {uid(OBSERVATION_NS, row_key(study, "labs", "HDL"))}
         associated_participant:
           expr: {participant}
-        associated_visit:
-          expr: {visit}
-        observation_type:
+{observed_at(study, "labs")}        observation_type:
           value: OMOP:3007070
         associated_assay:
           class_derivations:
@@ -309,7 +331,6 @@ def wbc(study):
     """WBC references a CBC assay instance through associated_assay."""
     t = study.tables["labs"]
     _, participant = ident(study, "labs")
-    visit = visit_ref(study, "labs")
     return f"""- class_derivations:
     MeasurementObservation:
       populated_from: {t}
@@ -318,9 +339,7 @@ def wbc(study):
           expr: {uid(OBSERVATION_NS, row_key(study, "labs", "WBC"))}
         associated_participant:
           expr: {participant}
-        associated_visit:
-          expr: {visit}
-        observation_type:
+{observed_at(study, "labs")}        observation_type:
           value: OMOP:3000905
         associated_assay:
           class_derivations:
@@ -345,8 +364,28 @@ def wbc(study):
 """
 
 
-def condition(study, label, status_col, concept_col, provenance, relationship=None):
-    """One Condition. relationship defaults to ONESELF for self-reported history."""
+def condition(
+    study,
+    label,
+    status_col,
+    concept_col,
+    provenance,
+    relationship=None,
+    visit_col=None,
+    age_start_col=None,
+    age_end_col=None,
+    evidence=None,
+):
+    """
+    One Condition. relationship defaults to ONESELF for self-reported history.
+
+    The temporal slots are optional because not every condition has a meaningful
+    one: family history is about a relative, so the participant's age at its
+    start is undefined, and an infarction is an event rather than a state that
+    resolves, so it has no end. Where a column is given, it is populated
+    straight through — the raw columns hold days and are already null when the
+    condition is absent, so no case() guard is needed to suppress them.
+    """
     t = study.tables["conditions"]
     _, participant = ident(study, "conditions")
     relationship_line = (
@@ -354,11 +393,21 @@ def condition(study, label, status_col, concept_col, provenance, relationship=No
         if relationship
         else f"          value: {v.ONESELF}"
     )
-    concept = (
-        f"          populated_from: {phv(study, 'conditions', concept_col)}"
-        if concept_col
-        else "          value: HP:0000822"
-    )
+
+    temporal = ""
+    if visit_col:
+        temporal += f"        associated_visit:\n          expr: {visit_ref(study, 'conditions', visit_col)}\n"
+    if age_start_col:
+        temporal += (
+            f"        age_at_condition_start:\n"
+            f"          populated_from: {phv(study, 'conditions', age_start_col)}\n"
+        )
+    if age_end_col:
+        temporal += (
+            f"        age_at_condition_end:\n"
+            f"          populated_from: {phv(study, 'conditions', age_end_col)}\n"
+        )
+
     return f"""- class_derivations:
     Condition:
       populated_from: {t}
@@ -367,15 +416,49 @@ def condition(study, label, status_col, concept_col, provenance, relationship=No
           expr: {uid(v.CONDITION_NS, row_key(study, "conditions", label))}
         associated_participant:
           expr: {participant}
-        condition_concept:
-{concept}
+{temporal}        condition_concept:
+          populated_from: {phv(study, 'conditions', concept_col)}
         condition_status:
           populated_from: {phv(study, "conditions", status_col)}
         condition_provenance:
           value: {provenance}
         relationship_to_participant:
 {relationship_line}
-"""
+{evidence or ""}"""
+
+
+def self_report_evidence():
+    """
+    Evidence for a condition taken from a questionnaire.
+
+    BDCHM ranges associated_evidence on Entity and marks it multivalued, but
+    MESA-ingest puts a bare descriptive string there. The corpus follows the
+    real spec rather than the model, so schema.py reports the cardinality
+    disagreement instead of the corpus hiding it.
+    """
+    return f'        associated_evidence:\n          value: "{v.SELF_REPORT_EVIDENCE}"\n'
+
+
+def mi_evidence(study):
+    """
+    Evidence for an infarction, which differs by where the record came from.
+
+    Half of the MIs are in the study record with an ECG behind them; the rest
+    are self-reported.
+
+    This branches with case() rather than the value_mappings the real specs use
+    for coded columns, because the two disagree on cardinality: value_mappings
+    honours BDCHM's multivalued declaration and emits a list, while a plain
+    `value:` emits a scalar. Mixing them would leave one slot carrying both
+    shapes, which schema.py types from the first record it sees and would
+    therefore get wrong for the rest. RTI emits the scalar, so the corpus does.
+    """
+    source = phv(study, "conditions", "HA_SOURCE")
+    return (
+        f"        associated_evidence:\n"
+        f"          expr: 'case(({{{source}}} == \"STUDY_RECORD\", \"{v.ECG_EVIDENCE}\"),"
+        f" ({{{source}}} == \"SELF_REPORT\", \"{v.SELF_REPORT_EVIDENCE}\"))'\n"
+    )
 
 
 def drug_exposure(study):
@@ -420,16 +503,42 @@ def build(study):
             extra='        qualifier:\n          value: "AVERAGE"\n',
         ),
         "wbc": wbc(study),
+        # Fasting glucose and HbA1c follow MESA-ingest's specs: the same OMOP
+        # observation types, units, and method_type strings.
+        "glucose": simple_measure(
+            study, "labs", "GLUCOSE", v.FASTING_GLUCOSE, "mg/dL",
+            extra=f'        method_type:\n          value: {v.GLUCOSE_METHOD}\n',
+        ),
+        "hba1c": simple_measure(
+            study, "labs", "HBA1C", v.HBA1C, "%",
+            extra=f'        method_type:\n          value: "{v.HBA1C_METHOD}"\n',
+        ),
         "cond_heart_failure": condition(
-            study, "heart_failure", "HEART_FAILURE", "HF_CONCEPT", v.SELF_REPORTED_CONDITION),
+            study, "heart_failure", "HEART_FAILURE", "HF_CONCEPT", v.SELF_REPORTED_CONDITION,
+            visit_col="HF_VISIT", age_start_col="HF_AGE_START",
+            evidence=self_report_evidence(),
+        ),
         "cond_family_stroke": condition(
             study, "family_stroke", "FAM_STROKE", "FS_CONCEPT", v.SELF_REPORTED_CONDITION,
             relationship=phv(study, "conditions", "FS_RELATIVE"),
+            visit_col="FS_VISIT",
+            evidence=self_report_evidence(),
         ),
         "cond_hypertension": condition(
-            study, "hypertension", "HYPERTENSION", None, v.SELF_REPORTED_CONDITION),
+            study, "hypertension", "HYPERTENSION", "HTN_CONCEPT", v.SELF_REPORTED_CONDITION,
+            visit_col="HTN_VISIT", age_start_col="HTN_AGE_START", age_end_col="HTN_AGE_END",
+            evidence=self_report_evidence(),
+        ),
+        "cond_diabetes": condition(
+            study, "diabetes", "DIABETES", "DM_CONCEPT", v.SELF_REPORTED_CONDITION,
+            visit_col="DM_VISIT", age_start_col="DM_AGE_START", age_end_col="DM_AGE_END",
+            evidence=self_report_evidence(),
+        ),
         "cond_heart_attack": condition(
-            study, "heart_attack", "HEART_ATTACK", "HA_CONCEPT", v.SELF_REPORTED_CONDITION),
+            study, "heart_attack", "HEART_ATTACK", "HA_CONCEPT", v.SELF_REPORTED_CONDITION,
+            visit_col="HA_VISIT", age_start_col="HA_AGE_START",
+            evidence=mi_evidence(study),
+        ),
         "drug_exposure": drug_exposure(study),
     }
     return specs
diff --git a/synthetic/specs/example_study_one/blood_pressure.yaml b/synthetic/specs/example_study_one/blood_pressure.yaml
index 9bb3b4e..6903309 100644
--- a/synthetic/specs/example_study_one/blood_pressure.yaml
+++ b/synthetic/specs/example_study_one/blood_pressure.yaml
@@ -17,6 +17,10 @@
                   expr: 'uuid5("https://w3id.org/bdchm/MeasurementObservation", str({phv10111300}) + ":Example Study One:" + str({phv10111301}) + ":systolic")'
                 associated_participant:
                   expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111300}) + ":Example Study One")'
+                associated_visit:
+                  expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111300}) + ":Example Study One VISIT " + str({phv10111301}))'
+                age_at_observation:
+                  populated_from: phv10111302
                 observation_type:
                   value: OMOP:4152194
                 body_position:
@@ -31,7 +35,7 @@
                         id:
                           expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111300}) + ":Example Study One:" + str({phv10111301}) + ":systolic")'
                         value_decimal:
-                          populated_from: phv10111305
+                          populated_from: phv10111306
                         unit:
                           value: "mm[Hg]"
           - MeasurementObservation:
@@ -41,6 +45,10 @@
                   expr: 'uuid5("https://w3id.org/bdchm/MeasurementObservation", str({phv10111300}) + ":Example Study One:" + str({phv10111301}) + ":diastolic")'
                 associated_participant:
                   expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111300}) + ":Example Study One")'
+                associated_visit:
+                  expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111300}) + ":Example Study One VISIT " + str({phv10111301}))'
+                age_at_observation:
+                  populated_from: phv10111302
                 observation_type:
                   value: OMOP:4154790
                 body_position:
@@ -55,6 +63,6 @@
                         id:
                           expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111300}) + ":Example Study One:" + str({phv10111301}) + ":diastolic")'
                         value_decimal:
-                          populated_from: phv10111306
+                          populated_from: phv10111307
                         unit:
                           value: "mm[Hg]"
diff --git a/synthetic/specs/example_study_one/bmi.yaml b/synthetic/specs/example_study_one/bmi.yaml
index 3002d9a..332826c 100644
--- a/synthetic/specs/example_study_one/bmi.yaml
+++ b/synthetic/specs/example_study_one/bmi.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111300}) + ":Example Study One")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111300}) + ":Example Study One VISIT " + str({phv10111301}))'
+        age_at_observation:
+          populated_from: phv10111302
         observation_type:
           value: OMOP:3038553
         value_quantity:
@@ -18,6 +20,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111300}) + ":Example Study One:" + str({phv10111301}) + ":BMI")'
                 value_decimal:
-                  populated_from: phv10111304
+                  populated_from: phv10111305
                 unit:
                   value: "kg/m2"
diff --git a/synthetic/specs/example_study_one/bun.yaml b/synthetic/specs/example_study_one/bun.yaml
index 74fb89a..723fe78 100644
--- a/synthetic/specs/example_study_one/bun.yaml
+++ b/synthetic/specs/example_study_one/bun.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111400}) + ":Example Study One")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111400}) + ":Example Study One VISIT " + str({phv10111401}))'
+        age_at_observation:
+          populated_from: phv10111402
         observation_type:
           value: OMOP:3013682
         qualifier:
@@ -20,6 +22,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111400}) + ":Example Study One:" + str({phv10111401}) + ":BUN")'
                 value_decimal:
-                  populated_from: phv10111404
+                  populated_from: phv10111405
                 unit:
                   value: "mg/dL"
diff --git a/synthetic/specs/example_study_one/cond_diabetes.yaml b/synthetic/specs/example_study_one/cond_diabetes.yaml
new file mode 100644
index 0000000..1eccb17
--- /dev/null
+++ b/synthetic/specs/example_study_one/cond_diabetes.yaml
@@ -0,0 +1,24 @@
+- class_derivations:
+    Condition:
+      populated_from: pht000115
+      slot_derivations:
+        id:
+          expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10111500}) + ":Example Study One:diabetes")'
+        associated_participant:
+          expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111500}) + ":Example Study One")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111500}) + ":Example Study One VISIT " + str({phv10111522}))'
+        age_at_condition_start:
+          populated_from: phv10111520
+        age_at_condition_end:
+          populated_from: phv10111521
+        condition_concept:
+          populated_from: phv10111517
+        condition_status:
+          populated_from: phv10111516
+        condition_provenance:
+          value: PATIENT_SELF-REPORTED_CONDITION
+        relationship_to_participant:
+          value: ONESELF
+        associated_evidence:
+          value: "self-report questionnaire"
diff --git a/synthetic/specs/example_study_one/cond_family_stroke.yaml b/synthetic/specs/example_study_one/cond_family_stroke.yaml
index e9f0aea..a7515e2 100644
--- a/synthetic/specs/example_study_one/cond_family_stroke.yaml
+++ b/synthetic/specs/example_study_one/cond_family_stroke.yaml
@@ -6,11 +6,15 @@
           expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10111500}) + ":Example Study One:family_stroke")'
         associated_participant:
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111500}) + ":Example Study One")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111500}) + ":Example Study One VISIT " + str({phv10111508}))'
         condition_concept:
-          populated_from: phv10111504
+          populated_from: phv10111506
         condition_status:
-          populated_from: phv10111503
+          populated_from: phv10111505
         condition_provenance:
           value: PATIENT_SELF-REPORTED_CONDITION
         relationship_to_participant:
-          populated_from: phv10111505
+          populated_from: phv10111507
+        associated_evidence:
+          value: "self-report questionnaire"
diff --git a/synthetic/specs/example_study_one/cond_heart_attack.yaml b/synthetic/specs/example_study_one/cond_heart_attack.yaml
index b9e4774..26562da 100644
--- a/synthetic/specs/example_study_one/cond_heart_attack.yaml
+++ b/synthetic/specs/example_study_one/cond_heart_attack.yaml
@@ -6,11 +6,17 @@
           expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10111500}) + ":Example Study One:heart_attack")'
         associated_participant:
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111500}) + ":Example Study One")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111500}) + ":Example Study One VISIT " + str({phv10111527}))'
+        age_at_condition_start:
+          populated_from: phv10111526
         condition_concept:
-          populated_from: phv10111508
+          populated_from: phv10111524
         condition_status:
-          populated_from: phv10111507
+          populated_from: phv10111523
         condition_provenance:
           value: PATIENT_SELF-REPORTED_CONDITION
         relationship_to_participant:
           value: ONESELF
+        associated_evidence:
+          expr: 'case(({phv10111525} == "STUDY_RECORD", "electrocardiogram"), ({phv10111525} == "SELF_REPORT", "self-report questionnaire"))'
diff --git a/synthetic/specs/example_study_one/cond_heart_failure.yaml b/synthetic/specs/example_study_one/cond_heart_failure.yaml
index 5bfeeb3..2693d26 100644
--- a/synthetic/specs/example_study_one/cond_heart_failure.yaml
+++ b/synthetic/specs/example_study_one/cond_heart_failure.yaml
@@ -6,6 +6,10 @@
           expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10111500}) + ":Example Study One:heart_failure")'
         associated_participant:
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111500}) + ":Example Study One")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111500}) + ":Example Study One VISIT " + str({phv10111504}))'
+        age_at_condition_start:
+          populated_from: phv10111503
         condition_concept:
           populated_from: phv10111502
         condition_status:
@@ -14,3 +18,5 @@
           value: PATIENT_SELF-REPORTED_CONDITION
         relationship_to_participant:
           value: ONESELF
+        associated_evidence:
+          value: "self-report questionnaire"
diff --git a/synthetic/specs/example_study_one/cond_hypertension.yaml b/synthetic/specs/example_study_one/cond_hypertension.yaml
index 060428d..d163168 100644
--- a/synthetic/specs/example_study_one/cond_hypertension.yaml
+++ b/synthetic/specs/example_study_one/cond_hypertension.yaml
@@ -6,11 +6,19 @@
           expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10111500}) + ":Example Study One:hypertension")'
         associated_participant:
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111500}) + ":Example Study One")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111500}) + ":Example Study One VISIT " + str({phv10111515}))'
+        age_at_condition_start:
+          populated_from: phv10111513
+        age_at_condition_end:
+          populated_from: phv10111514
         condition_concept:
-          value: HP:0000822
+          populated_from: phv10111510
         condition_status:
-          populated_from: phv10111506
+          populated_from: phv10111509
         condition_provenance:
           value: PATIENT_SELF-REPORTED_CONDITION
         relationship_to_participant:
           value: ONESELF
+        associated_evidence:
+          value: "self-report questionnaire"
diff --git a/synthetic/specs/example_study_one/glucose.yaml b/synthetic/specs/example_study_one/glucose.yaml
new file mode 100644
index 0000000..7b2da1e
--- /dev/null
+++ b/synthetic/specs/example_study_one/glucose.yaml
@@ -0,0 +1,27 @@
+- class_derivations:
+    MeasurementObservation:
+      populated_from: pht000114
+      slot_derivations:
+        id:
+          expr: 'uuid5("https://w3id.org/bdchm/MeasurementObservation", str({phv10111400}) + ":Example Study One:" + str({phv10111401}) + ":GLUCOSE")'
+        associated_participant:
+          expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111400}) + ":Example Study One")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111400}) + ":Example Study One VISIT " + str({phv10111401}))'
+        age_at_observation:
+          populated_from: phv10111402
+        observation_type:
+          value: OMOP:4156660
+        method_type:
+          value: CALIBRATED
+        value_quantity:
+          class_derivations:
+          - Quantity:
+              populated_from: pht000114
+              slot_derivations:
+                id:
+                  expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111400}) + ":Example Study One:" + str({phv10111401}) + ":GLUCOSE")'
+                value_decimal:
+                  populated_from: phv10111407
+                unit:
+                  value: "mg/dL"
diff --git a/synthetic/specs/example_study_one/hba1c.yaml b/synthetic/specs/example_study_one/hba1c.yaml
new file mode 100644
index 0000000..6870c3b
--- /dev/null
+++ b/synthetic/specs/example_study_one/hba1c.yaml
@@ -0,0 +1,27 @@
+- class_derivations:
+    MeasurementObservation:
+      populated_from: pht000114
+      slot_derivations:
+        id:
+          expr: 'uuid5("https://w3id.org/bdchm/MeasurementObservation", str({phv10111400}) + ":Example Study One:" + str({phv10111401}) + ":HBA1C")'
+        associated_participant:
+          expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111400}) + ":Example Study One")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111400}) + ":Example Study One VISIT " + str({phv10111401}))'
+        age_at_observation:
+          populated_from: phv10111402
+        observation_type:
+          value: OMOP:4184637
+        method_type:
+          value: "blood assay"
+        value_quantity:
+          class_derivations:
+          - Quantity:
+              populated_from: pht000114
+              slot_derivations:
+                id:
+                  expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111400}) + ":Example Study One:" + str({phv10111401}) + ":HBA1C")'
+                value_decimal:
+                  populated_from: phv10111408
+                unit:
+                  value: "%"
diff --git a/synthetic/specs/example_study_one/hdl.yaml b/synthetic/specs/example_study_one/hdl.yaml
index 7374b82..76a9e6b 100644
--- a/synthetic/specs/example_study_one/hdl.yaml
+++ b/synthetic/specs/example_study_one/hdl.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111400}) + ":Example Study One")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111400}) + ":Example Study One VISIT " + str({phv10111401}))'
+        age_at_observation:
+          populated_from: phv10111402
         observation_type:
           value: OMOP:3007070
         associated_assay:
@@ -49,8 +51,8 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111400}) + ":Example Study One:" + str({phv10111401}) + ":HDL")'
                 value_decimal:
-                  populated_from: phv10111402
-                operator:
                   populated_from: phv10111403
+                operator:
+                  populated_from: phv10111404
                 unit:
                   value: "mg/dL"
diff --git a/synthetic/specs/example_study_one/height.yaml b/synthetic/specs/example_study_one/height.yaml
index f44ebb2..df299ab 100644
--- a/synthetic/specs/example_study_one/height.yaml
+++ b/synthetic/specs/example_study_one/height.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111300}) + ":Example Study One")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111300}) + ":Example Study One VISIT " + str({phv10111301}))'
+        age_at_observation:
+          populated_from: phv10111302
         observation_type:
           value: OMOP:3036277
         value_quantity:
@@ -18,6 +20,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111300}) + ":Example Study One:" + str({phv10111301}) + ":HEIGHT_CM")'
                 value_decimal:
-                  populated_from: phv10111302
+                  populated_from: phv10111303
                 unit:
                   value: "cm"
diff --git a/synthetic/specs/example_study_one/wbc.yaml b/synthetic/specs/example_study_one/wbc.yaml
index ef3c46e..0188987 100644
--- a/synthetic/specs/example_study_one/wbc.yaml
+++ b/synthetic/specs/example_study_one/wbc.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111400}) + ":Example Study One")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111400}) + ":Example Study One VISIT " + str({phv10111401}))'
+        age_at_observation:
+          populated_from: phv10111402
         observation_type:
           value: OMOP:3000905
         associated_assay:
@@ -27,6 +29,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111400}) + ":Example Study One:" + str({phv10111401}) + ":WBC")'
                 value_decimal:
-                  populated_from: phv10111405
+                  populated_from: phv10111406
                 unit:
                   value: "10*3/uL"
diff --git a/synthetic/specs/example_study_one/weight.yaml b/synthetic/specs/example_study_one/weight.yaml
index a5e78c4..9172e16 100644
--- a/synthetic/specs/example_study_one/weight.yaml
+++ b/synthetic/specs/example_study_one/weight.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10111300}) + ":Example Study One")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10111300}) + ":Example Study One VISIT " + str({phv10111301}))'
+        age_at_observation:
+          populated_from: phv10111302
         observation_type:
           value: OMOP:3025315
         value_quantity:
@@ -18,6 +20,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10111300}) + ":Example Study One:" + str({phv10111301}) + ":WEIGHT_KG")'
                 value_decimal:
-                  populated_from: phv10111303
+                  populated_from: phv10111304
                 unit:
                   value: "kg"
diff --git a/synthetic/specs/example_study_two/blood_pressure.yaml b/synthetic/specs/example_study_two/blood_pressure.yaml
index 043fbff..127e130 100644
--- a/synthetic/specs/example_study_two/blood_pressure.yaml
+++ b/synthetic/specs/example_study_two/blood_pressure.yaml
@@ -17,6 +17,10 @@
                   expr: 'uuid5("https://w3id.org/bdchm/MeasurementObservation", str({phv10212300}) + ":Example Study Two:" + str({phv10212301}) + ":systolic")'
                 associated_participant:
                   expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212300}) + ":Example Study Two")'
+                associated_visit:
+                  expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212300}) + ":Example Study Two VISIT " + str({phv10212301}))'
+                age_at_observation:
+                  populated_from: phv10212302
                 observation_type:
                   value: OMOP:4152194
                 body_position:
@@ -31,7 +35,7 @@
                         id:
                           expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212300}) + ":Example Study Two:" + str({phv10212301}) + ":systolic")'
                         value_decimal:
-                          populated_from: phv10212305
+                          populated_from: phv10212306
                         unit:
                           value: "mm[Hg]"
           - MeasurementObservation:
@@ -41,6 +45,10 @@
                   expr: 'uuid5("https://w3id.org/bdchm/MeasurementObservation", str({phv10212300}) + ":Example Study Two:" + str({phv10212301}) + ":diastolic")'
                 associated_participant:
                   expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212300}) + ":Example Study Two")'
+                associated_visit:
+                  expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212300}) + ":Example Study Two VISIT " + str({phv10212301}))'
+                age_at_observation:
+                  populated_from: phv10212302
                 observation_type:
                   value: OMOP:4154790
                 body_position:
@@ -55,6 +63,6 @@
                         id:
                           expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212300}) + ":Example Study Two:" + str({phv10212301}) + ":diastolic")'
                         value_decimal:
-                          populated_from: phv10212306
+                          populated_from: phv10212307
                         unit:
                           value: "mm[Hg]"
diff --git a/synthetic/specs/example_study_two/bmi.yaml b/synthetic/specs/example_study_two/bmi.yaml
index 97e28fb..341d3c6 100644
--- a/synthetic/specs/example_study_two/bmi.yaml
+++ b/synthetic/specs/example_study_two/bmi.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212300}) + ":Example Study Two")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212300}) + ":Example Study Two VISIT " + str({phv10212301}))'
+        age_at_observation:
+          populated_from: phv10212302
         observation_type:
           value: OMOP:3038553
         value_quantity:
@@ -18,4 +20,4 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212300}) + ":Example Study Two:" + str({phv10212301}) + ":BMI")'
                 value_concept:
-                  populated_from: phv10212304
+                  populated_from: phv10212305
diff --git a/synthetic/specs/example_study_two/bun.yaml b/synthetic/specs/example_study_two/bun.yaml
index 4b26615..d728ecb 100644
--- a/synthetic/specs/example_study_two/bun.yaml
+++ b/synthetic/specs/example_study_two/bun.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212400}) + ":Example Study Two")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212400}) + ":Example Study Two VISIT " + str({phv10212401}))'
+        age_at_observation:
+          populated_from: phv10212402
         observation_type:
           value: OMOP:3013682
         qualifier:
@@ -20,6 +22,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212400}) + ":Example Study Two:" + str({phv10212401}) + ":BUN")'
                 value_decimal:
-                  populated_from: phv10212404
+                  populated_from: phv10212405
                 unit:
                   value: "mg/dL"
diff --git a/synthetic/specs/example_study_two/cond_diabetes.yaml b/synthetic/specs/example_study_two/cond_diabetes.yaml
new file mode 100644
index 0000000..bd36226
--- /dev/null
+++ b/synthetic/specs/example_study_two/cond_diabetes.yaml
@@ -0,0 +1,24 @@
+- class_derivations:
+    Condition:
+      populated_from: pht000125
+      slot_derivations:
+        id:
+          expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10212500}) + ":Example Study Two:diabetes")'
+        associated_participant:
+          expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212500}) + ":Example Study Two")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212500}) + ":Example Study Two VISIT " + str({phv10212522}))'
+        age_at_condition_start:
+          populated_from: phv10212520
+        age_at_condition_end:
+          populated_from: phv10212521
+        condition_concept:
+          populated_from: phv10212517
+        condition_status:
+          populated_from: phv10212516
+        condition_provenance:
+          value: PATIENT_SELF-REPORTED_CONDITION
+        relationship_to_participant:
+          value: ONESELF
+        associated_evidence:
+          value: "self-report questionnaire"
diff --git a/synthetic/specs/example_study_two/cond_family_stroke.yaml b/synthetic/specs/example_study_two/cond_family_stroke.yaml
index c51161a..b92db66 100644
--- a/synthetic/specs/example_study_two/cond_family_stroke.yaml
+++ b/synthetic/specs/example_study_two/cond_family_stroke.yaml
@@ -6,11 +6,15 @@
           expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10212500}) + ":Example Study Two:family_stroke")'
         associated_participant:
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212500}) + ":Example Study Two")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212500}) + ":Example Study Two VISIT " + str({phv10212508}))'
         condition_concept:
-          populated_from: phv10212504
+          populated_from: phv10212506
         condition_status:
-          populated_from: phv10212503
+          populated_from: phv10212505
         condition_provenance:
           value: PATIENT_SELF-REPORTED_CONDITION
         relationship_to_participant:
-          populated_from: phv10212505
+          populated_from: phv10212507
+        associated_evidence:
+          value: "self-report questionnaire"
diff --git a/synthetic/specs/example_study_two/cond_heart_attack.yaml b/synthetic/specs/example_study_two/cond_heart_attack.yaml
index e1d3965..2ae326f 100644
--- a/synthetic/specs/example_study_two/cond_heart_attack.yaml
+++ b/synthetic/specs/example_study_two/cond_heart_attack.yaml
@@ -6,11 +6,17 @@
           expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10212500}) + ":Example Study Two:heart_attack")'
         associated_participant:
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212500}) + ":Example Study Two")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212500}) + ":Example Study Two VISIT " + str({phv10212527}))'
+        age_at_condition_start:
+          populated_from: phv10212526
         condition_concept:
-          populated_from: phv10212508
+          populated_from: phv10212524
         condition_status:
-          populated_from: phv10212507
+          populated_from: phv10212523
         condition_provenance:
           value: PATIENT_SELF-REPORTED_CONDITION
         relationship_to_participant:
           value: ONESELF
+        associated_evidence:
+          expr: 'case(({phv10212525} == "STUDY_RECORD", "electrocardiogram"), ({phv10212525} == "SELF_REPORT", "self-report questionnaire"))'
diff --git a/synthetic/specs/example_study_two/cond_heart_failure.yaml b/synthetic/specs/example_study_two/cond_heart_failure.yaml
index 16afe07..16b29aa 100644
--- a/synthetic/specs/example_study_two/cond_heart_failure.yaml
+++ b/synthetic/specs/example_study_two/cond_heart_failure.yaml
@@ -6,6 +6,10 @@
           expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10212500}) + ":Example Study Two:heart_failure")'
         associated_participant:
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212500}) + ":Example Study Two")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212500}) + ":Example Study Two VISIT " + str({phv10212504}))'
+        age_at_condition_start:
+          populated_from: phv10212503
         condition_concept:
           populated_from: phv10212502
         condition_status:
@@ -14,3 +18,5 @@
           value: PATIENT_SELF-REPORTED_CONDITION
         relationship_to_participant:
           value: ONESELF
+        associated_evidence:
+          value: "self-report questionnaire"
diff --git a/synthetic/specs/example_study_two/cond_hypertension.yaml b/synthetic/specs/example_study_two/cond_hypertension.yaml
index b980ae9..68ebde2 100644
--- a/synthetic/specs/example_study_two/cond_hypertension.yaml
+++ b/synthetic/specs/example_study_two/cond_hypertension.yaml
@@ -6,11 +6,19 @@
           expr: 'uuid5("https://w3id.org/bdchm/Condition", str({phv10212500}) + ":Example Study Two:hypertension")'
         associated_participant:
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212500}) + ":Example Study Two")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212500}) + ":Example Study Two VISIT " + str({phv10212515}))'
+        age_at_condition_start:
+          populated_from: phv10212513
+        age_at_condition_end:
+          populated_from: phv10212514
         condition_concept:
-          value: HP:0000822
+          populated_from: phv10212510
         condition_status:
-          populated_from: phv10212506
+          populated_from: phv10212509
         condition_provenance:
           value: PATIENT_SELF-REPORTED_CONDITION
         relationship_to_participant:
           value: ONESELF
+        associated_evidence:
+          value: "self-report questionnaire"
diff --git a/synthetic/specs/example_study_two/glucose.yaml b/synthetic/specs/example_study_two/glucose.yaml
new file mode 100644
index 0000000..9fdde6b
--- /dev/null
+++ b/synthetic/specs/example_study_two/glucose.yaml
@@ -0,0 +1,27 @@
+- class_derivations:
+    MeasurementObservation:
+      populated_from: pht000124
+      slot_derivations:
+        id:
+          expr: 'uuid5("https://w3id.org/bdchm/MeasurementObservation", str({phv10212400}) + ":Example Study Two:" + str({phv10212401}) + ":GLUCOSE")'
+        associated_participant:
+          expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212400}) + ":Example Study Two")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212400}) + ":Example Study Two VISIT " + str({phv10212401}))'
+        age_at_observation:
+          populated_from: phv10212402
+        observation_type:
+          value: OMOP:4156660
+        method_type:
+          value: CALIBRATED
+        value_quantity:
+          class_derivations:
+          - Quantity:
+              populated_from: pht000124
+              slot_derivations:
+                id:
+                  expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212400}) + ":Example Study Two:" + str({phv10212401}) + ":GLUCOSE")'
+                value_decimal:
+                  populated_from: phv10212407
+                unit:
+                  value: "mg/dL"
diff --git a/synthetic/specs/example_study_two/hba1c.yaml b/synthetic/specs/example_study_two/hba1c.yaml
new file mode 100644
index 0000000..9afc2ba
--- /dev/null
+++ b/synthetic/specs/example_study_two/hba1c.yaml
@@ -0,0 +1,27 @@
+- class_derivations:
+    MeasurementObservation:
+      populated_from: pht000124
+      slot_derivations:
+        id:
+          expr: 'uuid5("https://w3id.org/bdchm/MeasurementObservation", str({phv10212400}) + ":Example Study Two:" + str({phv10212401}) + ":HBA1C")'
+        associated_participant:
+          expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212400}) + ":Example Study Two")'
+        associated_visit:
+          expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212400}) + ":Example Study Two VISIT " + str({phv10212401}))'
+        age_at_observation:
+          populated_from: phv10212402
+        observation_type:
+          value: OMOP:4184637
+        method_type:
+          value: "blood assay"
+        value_quantity:
+          class_derivations:
+          - Quantity:
+              populated_from: pht000124
+              slot_derivations:
+                id:
+                  expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212400}) + ":Example Study Two:" + str({phv10212401}) + ":HBA1C")'
+                value_decimal:
+                  populated_from: phv10212408
+                unit:
+                  value: "%"
diff --git a/synthetic/specs/example_study_two/hdl.yaml b/synthetic/specs/example_study_two/hdl.yaml
index cccf6c0..00d0a59 100644
--- a/synthetic/specs/example_study_two/hdl.yaml
+++ b/synthetic/specs/example_study_two/hdl.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212400}) + ":Example Study Two")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212400}) + ":Example Study Two VISIT " + str({phv10212401}))'
+        age_at_observation:
+          populated_from: phv10212402
         observation_type:
           value: OMOP:3007070
         associated_assay:
@@ -49,8 +51,8 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212400}) + ":Example Study Two:" + str({phv10212401}) + ":HDL")'
                 value_decimal:
-                  populated_from: phv10212402
-                operator:
                   populated_from: phv10212403
+                operator:
+                  populated_from: phv10212404
                 unit:
                   value: "mg/dL"
diff --git a/synthetic/specs/example_study_two/height.yaml b/synthetic/specs/example_study_two/height.yaml
index fdca786..4657e1f 100644
--- a/synthetic/specs/example_study_two/height.yaml
+++ b/synthetic/specs/example_study_two/height.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212300}) + ":Example Study Two")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212300}) + ":Example Study Two VISIT " + str({phv10212301}))'
+        age_at_observation:
+          populated_from: phv10212302
         observation_type:
           value: OMOP:3036277
         value_quantity:
@@ -18,6 +20,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212300}) + ":Example Study Two:" + str({phv10212301}) + ":HEIGHT_CM")'
                 value_decimal:
-                  populated_from: phv10212302
+                  populated_from: phv10212303
                 unit:
                   value: "cm"
diff --git a/synthetic/specs/example_study_two/wbc.yaml b/synthetic/specs/example_study_two/wbc.yaml
index 6f34c7a..1295b42 100644
--- a/synthetic/specs/example_study_two/wbc.yaml
+++ b/synthetic/specs/example_study_two/wbc.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212400}) + ":Example Study Two")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212400}) + ":Example Study Two VISIT " + str({phv10212401}))'
+        age_at_observation:
+          populated_from: phv10212402
         observation_type:
           value: OMOP:3000905
         associated_assay:
@@ -27,6 +29,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212400}) + ":Example Study Two:" + str({phv10212401}) + ":WBC")'
                 value_decimal:
-                  populated_from: phv10212405
+                  populated_from: phv10212406
                 unit:
                   value: "10*3/uL"
diff --git a/synthetic/specs/example_study_two/weight.yaml b/synthetic/specs/example_study_two/weight.yaml
index 930ac0d..c228bb6 100644
--- a/synthetic/specs/example_study_two/weight.yaml
+++ b/synthetic/specs/example_study_two/weight.yaml
@@ -8,6 +8,8 @@
           expr: 'uuid5("https://w3id.org/bdchm/Participant", str({phv10212300}) + ":Example Study Two")'
         associated_visit:
           expr: 'uuid5("https://w3id.org/bdchm/Visit", str({phv10212300}) + ":Example Study Two VISIT " + str({phv10212301}))'
+        age_at_observation:
+          populated_from: phv10212302
         observation_type:
           value: OMOP:3025315
         value_quantity:
@@ -18,6 +20,6 @@
                 id:
                   expr: 'uuid5("https://w3id.org/bdchm/Quantity", str({phv10212300}) + ":Example Study Two:" + str({phv10212301}) + ":WEIGHT_KG")'
                 value_decimal:
-                  populated_from: phv10212303
+                  populated_from: phv10212304
                 unit:
                   value: "kg"
diff --git a/synthetic/validate.py b/synthetic/validate.py
index 42c9a37..116a356 100644
--- a/synthetic/validate.py
+++ b/synthetic/validate.py
@@ -36,12 +36,125 @@ def exact(label, actual, expected):
         failures.append(label)
 
 
+def at_least(label, actual, floor):
+    """Record a coverage check: enough distinct values to be worth querying."""
+    ok = actual >= floor
+    notes.append(f"{'ok ' if ok else 'FAIL'} {label}: {actual} (at least {floor})")
+    if not ok:
+        failures.append(label)
+
+
 def fraction(items, predicate):
     """Return the proportion of items satisfying the predicate."""
     items = list(items)
     return sum(1 for x in items if predicate(x)) / len(items) if items else 0.0
 
 
+def diagnosed(entry):
+    """Whether a condition entry represents a diagnosis rather than a screen."""
+    return entry["status"] in (v.PRESENT, v.HISTORICAL)
+
+
+def check_temporal(participants):
+    """
+    Every temporal anchor must be usable, not merely present.
+
+    A start age nobody could have reached, an end before its start, or a
+    recording visit the participant never attended would each pass a
+    completeness check while making the slot useless for a cohort query.
+    """
+    bad_order = bad_floor = bad_visit = bad_absent = orphan_end = 0
+
+    for p in participants:
+        ages = {x.number: x.age_days for x in p.visits}
+        for entry in p.conditions.values():
+            start, end, visit = entry["age_start"], entry["age_end"], entry["visit"]
+
+            if not diagnosed(entry) and (start is not None or end is not None):
+                bad_absent += 1
+            if start is not None and start < pop.MIN_DIAGNOSIS_AGE_DAYS:
+                bad_floor += 1
+            if end is not None:
+                if start is None:
+                    orphan_end += 1
+                elif end <= start:
+                    bad_order += 1
+            # A condition recorded at a visit must name one the participant
+            # attended, and cannot be diagnosed after that visit took place.
+            if visit is not None:
+                if visit not in ages:
+                    bad_visit += 1
+                elif start is not None and start > ages[visit]:
+                    bad_visit += 1
+
+    exact("conditions dated while absent", bad_absent, 0)
+    exact("diagnoses before the minimum age", bad_floor, 0)
+    exact("conditions ending before they started", bad_order, 0)
+    exact("conditions ending without a start", orphan_end, 0)
+    exact("conditions recorded at an unattended or earlier visit", bad_visit, 0)
+
+
+def check_before_and_after(participants):
+    """
+    Check the point of the temporal metadata: measurements sort by diagnosis.
+
+    A condition only elevates the measures it explains once it has been
+    diagnosed, so for participants diagnosed mid-study the glucose values
+    before the diagnosis should look like the corpus as a whole and the ones
+    after should look like the diabetic subgroup. If these two converge, the
+    temporal slots are decoration.
+    """
+    before, after = [], []
+    for p in participants:
+        entry = p.conditions["diabetes"]
+        start = entry["age_start"]
+        if entry["status"] != v.PRESENT or start is None or not p.visits:
+            continue
+        if start <= p.visits[0].age_days:
+            continue  # prevalent: nothing falls before the diagnosis
+        for visit in p.visits:
+            if visit.glucose is None:
+                continue
+            (before if visit.age_days < start else after).append(visit.glucose)
+
+    # The pathological glucose range starts at 126, so 'pathological draw' and
+    # 'at or above 126' are the same set.
+    exact("incident diabetes with glucose on both sides", len(before) > 0 and len(after) > 0, True)
+    if before and after:
+        # Both samples are small — a few dozen values each — so these carry a
+        # wider tolerance than the corpus-wide distribution checks.
+        near("glucose pathological before diagnosis",
+             fraction(before, lambda x: x >= 126), pop.PATHOLOGY_RATE, tolerance=0.10)
+        near("glucose pathological after diagnosis",
+             fraction(after, lambda x: x >= 126), pop.COMORBID_PATHOLOGY_RATE, tolerance=0.18)
+
+
+def check_code_coverage(participants):
+    """
+    Enough distinct concepts and source codes to test a broad cohort definition.
+
+    Floors rather than exact counts: the rarest subtypes carry weights low
+    enough that whether they appear at all is a coin flip, and a corpus that
+    failed because one 0.5% subtype went unsampled would be failing on noise.
+    """
+    htn = [p.conditions["hypertension"] for p in participants]
+    dm = [p.conditions["diabetes"] for p in participants]
+
+    at_least("distinct hypertension concepts",
+             len({e["concept"] for e in htn if diagnosed(e)}), 10)
+    at_least("distinct hypertension ICD-10-CM codes",
+             len({e["icd10"] for e in htn if e["icd10"]}), 8)
+    at_least("distinct diabetes subtypes",
+             len({e["subtype"] for e in dm if diagnosed(e)}), 8)
+    at_least("distinct diabetes ICD-10-CM codes",
+             len({e["icd10"] for e in dm if e["icd10"]}), 18)
+
+    # Source codes belong to diagnoses. A screening answer of 'no' records the
+    # concept that was screened for and nothing else.
+    stray = sum(1 for e in htn + dm if not diagnosed(e) and (e["icd10"] or e["snomed"]))
+    exact("source codes on undiagnosed conditions", stray, 0)
+
+
 def main():
     """Run every check and report."""
     people, participants = pop.build()
@@ -97,6 +210,23 @@ def main():
         near(f"{label}: heart attack PRESENT",
              fraction(group, lambda p: p.conditions["heart_attack"]["status"] == v.PRESENT), 0.10)
 
+        # Diabetes is drawn as 20% diagnosed and 2% unknown; the diagnosed split
+        # into PRESENT and the HISTORICAL 'in remission' subtype, so check the
+        # two together rather than pinning a share to one subtype's weight.
+        near(f"{label}: diabetes diagnosed",
+             fraction(group, lambda p: diagnosed(p.conditions["diabetes"])), 0.20)
+        near(f"{label}: diabetes UNKNOWN",
+             fraction(group, lambda p: p.conditions["diabetes"]["status"] == v.UNKNOWN), 0.02)
+
+        # Incident diagnoses are what make a before-and-after query meaningful.
+        dx = [e for p in group for e in p.conditions.values() if e["age_start"] is not None]
+        incident = [
+            e for p in group for e in p.conditions.values()
+            if e["age_start"] is not None and p.visits and e["age_start"] > p.visits[0].age_days
+        ]
+        near(f"{label}: diagnoses made during follow-up",
+             len(incident) / len(dx) if dx else 0.0, pop.INCIDENT_FRACTION, tolerance=0.07)
+
         mi = [p for p in group if p.conditions["heart_attack"]["status"] == v.PRESENT]
         near(f"{label}: MI from study record",
              fraction(mi, lambda p: p.conditions["heart_attack"]["from_study_record"]), 0.50, tolerance=0.15)
@@ -122,7 +252,8 @@ def main():
         getattr(x, field)
         for p in participants
         for x in p.visits
-        for field in ("height_cm", "weight_kg", "systolic", "diastolic", "hdl", "bun", "wbc")
+        for field in ("height_cm", "weight_kg", "systolic", "diastolic", "hdl",
+                      "bun", "wbc", "glucose", "hba1c")
     ]
     near("null rate", sum(1 for c in cells if c is None) / len(cells), pop.NULL_RATE, tolerance=0.002)
 
@@ -139,6 +270,10 @@ def main():
         for concept in v.CALCIUM_CHANNEL_BLOCKERS:
             near(f"CCB share {concept}", ccb[concept] / total, 0.25, tolerance=0.09)
 
+    check_temporal(participants)
+    check_before_and_after(participants)
+    check_code_coverage(participants)
+
     print("\n".join(notes))
     print()
     if failures:
diff --git a/synthetic/vocab.py b/synthetic/vocab.py
index e67b744..619ed8a 100644
--- a/synthetic/vocab.py
+++ b/synthetic/vocab.py
@@ -2,11 +2,18 @@
 """
 Coded values used by the synthetic corpus.
 
-Every CURIE here is attested in RTI's priority_variables_transform specs or in
-BDCHM itself, so the corpus resolves against the same vocabulary as real
-harmonized data. Nothing in this file is invented.
+Every CURIE here is attested in one of three places, so the corpus resolves
+against the same vocabulary as real harmonized data. Nothing in this file is
+invented:
+
+  1. RTI's priority_variables_transform specs
+  2. BDCHM itself
+  3. The BDC cohort-readiness code-set reference, for the hypertension and
+     Type 2 diabetes subtypes (see CODE_SET_REFERENCE below)
 """
 
+from dataclasses import dataclass
+
 # Demography — BDCHM RaceEnum / EthnicityEnum / SexEnum meanings
 MALE = "OMOP:8507"
 FEMALE = "OMOP:8532"
@@ -43,7 +50,6 @@
     "MONDO:0011057",
     "MONDO:0013792",
 ]
-HYPERTENSION = "HP:0000822"
 HEART_ATTACK = ["MONDO:0005068", "MONDO:0006803"]
 
 # Observation types — from blood_pressure.yaml and the lab specs
@@ -82,12 +88,28 @@
 # even though it is required; a reference corpus should populate it.
 VISIT_PROVENANCE = "CASE_REPORT_FORM"
 
+# Glycemic measures. Unlike HDL, BUN and WBC — whose OMOP codes come from RTI's
+# specs and are not in the model — these two are permissible values of BDCHM's
+# MeasurementObservationTypeEnum (FAST_GLUC_BLD, HEMO_A1C) *and* are what
+# MESA-ingest/fast_gluc_bld.yaml and hemo_a1c.yaml emit. Units and method_type
+# are taken from those same specs.
+FASTING_GLUCOSE = "OMOP:4156660"
+HBA1C = "OMOP:4184637"
+GLUCOSE_METHOD = "CALIBRATED"
+HBA1C_METHOD = "blood assay"
+
 # Assay methods — BDCHM AssayMethodEnum resolves through MMO. No MMO term is
 # attested in the real specs, so these were looked up in MMO directly rather
 # than invented.
 ASSAY_METHOD_HDL = "MMO:0000133"  # serum high-density lipoprotein-cholesterol measurement test
 ASSAY_METHOD_WBC = "MMO:0000533"  # white blood cell counting method
 
+# Evidence supporting a condition assertion. BDCHM ranges associated_evidence on
+# Entity, but MESA-ingest puts a bare descriptive string there, so this is what
+# real harmonized data actually carries.
+SELF_REPORT_EVIDENCE = "self-report questionnaire"
+ECG_EVIDENCE = "electrocardiogram"
+
 # Relationship of a condition to the participant. ONESELF has no OMOP meaning
 # in BDCHM — it is a bare permissible value.
 ONESELF = "ONESELF"
@@ -96,3 +118,126 @@
 DRUG_EXPOSURE_NS = "https://w3id.org/bdchm/DrugExposure"
 OBSERVATION_SET_NS = "https://w3id.org/bdchm/MeasurementObservationSet"
 CAUSE_OF_DEATH_NS = "https://w3id.org/bdchm/CauseOfDeath"
+
+
+# --- Hypertension and Type 2 diabetes subtypes ------------------------------
+#
+# Source: the BDC cohort-readiness code-set reference, which lists MONDO, HPO,
+# SNOMED CT and ICD-10-CM for each subtype.
+CODE_SET_REFERENCE = (
+    "https://docs.google.com/spreadsheets/d/1AciqkeEaQfA9IptDpUI9yQYYzN7PWnt7"
+)
+
+
+@dataclass(frozen=True)
+class Subtype:
+    """
+    One row of the code-set reference.
+
+    `concept` is what BDCHM's condition_concept takes — MONDO where the
+    reference gives one, HPO otherwise, since ConditionConceptEnum is the union
+    of the two. `snomed` and `icd10` have no slot anywhere in BDCHM, so they
+    live only in the raw dbGaP-style tables, which is where source terminology
+    sits in real data too.
+
+    Several reference cells name a hierarchy rather than a term ("MONDO:0005148
+    hierarchy", "44054006 hierarchy + renal complication descendants") or a
+    wildcard rather than a code ("E11.3*"). Those are recorded as None or as an
+    empty tuple rather than guessed at: a subtype with no concrete source code
+    simply emits none, which is a shape real data has as well.
+    """
+
+    label: str
+    concept: str
+    hpo: str | None
+    snomed: str | None
+    icd10: tuple[str, ...]
+    weight: float
+
+
+# Weights are illustrative. The ordering is real — essential hypertension
+# dominates any hypertensive population — but the numbers are not drawn from any
+# prevalence study and must not be read as a finding.
+#
+# Pregnancy-related hypertension (MONDO:0005081) is in the reference but is left
+# out here: both cohorts enrol at 45-78, so no participant is plausibly
+# pregnancy-hypertensive during follow-up.
+HYPERTENSION_SUBTYPES = (
+    Subtype("essential (primary) hypertension",
+            "MONDO:0001134", "HP:0000822", "59621000", ("I10",), 0.56),
+    Subtype("systemic hypertensive disorder, unspecified",
+            "MONDO:0005044", "HP:0000822", "38341003", ("I10",), 0.15),
+    Subtype("hypertensive heart disease",
+            "MONDO:0001302", "HP:0000822", "64715009", ("I11.0", "I11.9"), 0.07),
+    Subtype("secondary hypertension",
+            "MONDO:0001200", "HP:0000822", "31992008",
+            ("I15.0", "I15.1", "I15.2", "I15.8", "I15.9"), 0.05),
+    Subtype("resistant hypertension",
+            "MONDO:0100078", None, "461301000124109", ("I1A.0",), 0.04),
+    Subtype("renovascular hypertension",
+            "MONDO:0006947", "HP:0100817", "123799005", ("I15.0",), 0.03),
+    Subtype("renal hypertension",
+            "MONDO:0001105", "HP:0000822", "28119000", ("I15.0", "I15.1"), 0.03),
+    Subtype("pulmonary arterial hypertension",
+            "MONDO:0015924", "HP:0002092", "11399002", (), 0.02),
+    Subtype("hypertensive urgency",
+            "MONDO:1030007", "HP:0100735", "443482000", ("I16.0",), 0.015),
+    Subtype("malignant hypertension",
+            "MONDO:0006846", "HP:0100735", "70272006", (), 0.01),
+    # The reference gives no single MONDO term for hypertensive crisis, only a
+    # pointer into the hierarchy — so this one is HPO-coded, which BDCHM allows.
+    Subtype("hypertensive crisis",
+            "HP:0100735", "HP:0100735", "706882009", ("I16.0", "I16.1", "I16.9"), 0.01),
+    Subtype("portal hypertension",
+            "MONDO:0005080", "HP:0001409", "34742003", ("K76.6",), 0.01),
+    Subtype("hypertensive encephalopathy",
+            "MONDO:0006796", None, "50490005", (), 0.005),
+)
+
+# The screening concept for participants who answered no: a questionnaire asks
+# about hypertension in general, and a subtype is only recorded once diagnosed.
+HYPERTENSION_ROOT = HYPERTENSION_SUBTYPES[1]
+
+# Type 2 diabetes. The reference gives MONDO:0005148 for nearly every row and
+# puts the complication-level detail in ICD-10-CM, so that is where the
+# granularity in this corpus lives too.
+#
+# T2D with ophthalmic complications is in the reference but omitted here: its
+# ICD-10-CM cell is the bare wildcard "E11.3*" and its MONDO is the shared
+# root, so it would be indistinguishable from every other T2D record.
+DIABETES_SUBTYPES = (
+    Subtype("Type 2 diabetes without complications",
+            "MONDO:0005148", "HP:0005978", "44054006", ("E11.9",), 0.37),
+    Subtype("Type 2 diabetes with kidney complications",
+            "MONDO:0005148", "HP:0005978", None,
+            ("E11.21", "E11.22", "E11.29"), 0.11),
+    Subtype("Type 2 diabetes with neurologic complications",
+            "MONDO:0005148", "HP:0005978", "421326000",
+            ("E11.40", "E11.41", "E11.42", "E11.43", "E11.44", "E11.49"), 0.11),
+    Subtype("Type 2 diabetes with other specified complications",
+            "MONDO:0005148", "HP:0005978", None,
+            ("E11.610", "E11.618", "E11.620", "E11.621", "E11.622", "E11.628",
+             "E11.630", "E11.638", "E11.641", "E11.649", "E11.65", "E11.69"), 0.09),
+    # In remission is the one subtype that resolves, and is what gives the
+    # corpus its Conditions with an age_at_condition_end.
+    Subtype("Type 2 diabetes in remission",
+            "MONDO:0005148", "HP:0005978", None, ("E11.A",), 0.08),
+    Subtype("Type 2 diabetes with unspecified complications",
+            "MONDO:0005148", "HP:0005978", None, ("E11.8",), 0.07),
+    Subtype("Type 2 diabetes with circulatory complications",
+            "MONDO:0005148", "HP:0005978", None,
+            ("E11.51", "E11.52", "E11.59"), 0.07),
+    # MONDO:0005015 is what MESA-ingest/diabetes.yaml actually emits, so a
+    # portal built on this corpus meets the same broad code real data carries.
+    Subtype("diabetes mellitus, type unspecified",
+            "MONDO:0005015", "HP:0000819", "73211009", (), 0.05),
+    Subtype("Type 2 diabetes with hyperosmolarity",
+            "MONDO:0005148", "HP:0005978", None, ("E11.00", "E11.01"), 0.02),
+    Subtype("Type 2 diabetes with ketoacidosis",
+            "MONDO:0005148", "HP:0005978", None, ("E11.10", "E11.11"), 0.02),
+    Subtype("lipoatrophic diabetes",
+            "MONDO:0005827", "HP:0005978", "127012008", (), 0.01),
+)
+
+DIABETES_ROOT = DIABETES_SUBTYPES[0]
+DIABETES_REMISSION = DIABETES_SUBTYPES[4]