feat(doctor-report): add workouts + lab trends + self-context, align adherence rounding

Surface actual workout sessions and a per-analyte lab trend in the doctor
report PDF + FHIR bundle (gated on their modules), carry the user's stored
conditions/allergies through to the report and FHIR Condition/AllergyIntolerance,
and round medication adherence to the app's integer convention so the PDF, FHIR
and in-app numbers agree.

src/lib/doctor-report-data.ts

@@ -65,6 +65,29 @@ export interface DoctorReportCompliance {
   missed: number;
 }
 
+/**
+ * Canonical adherence-rate rounding for the doctor-report export surfaces.
+ *
+ * The PDF table, the PDF clinical-summary headline, the FHIR adherence
+ * Observation, and the GLP-1 block all derive the rate from the SAME ledger
+ * denominator (`taken / total`, `total = taken + missed`). They MUST round it
+ * the same way the app does so a clinician comparing the export to the in-app
+ * card never sees a presentational divergence (87 % on the card, 87.3 % on the
+ * PDF). The app convention is `round(100 · taken / denominator)` capped at 100
+ * (see `dose-history-ledger-compute.ts` + `tallyComplianceFromLedger`) — an
+ * integer percent. This is the one source of truth for every export surface.
+ *
+ * Returns `null` when `total <= 0` so callers render the "no expected dose"
+ * placeholder instead of a misleading `0` or `100`.
+ */
+export function adherenceRatePercent(
+  taken: number,
+  total: number,
+): number | null {
+  if (total <= 0) return null;
+  return Math.min(100, Math.round((taken / total) * 100));
+}
+
 export interface DoctorReportMood {
   avg: number;
   min: number;

src/lib/doctor-report-pdf-core.ts

@@ -18,7 +18,10 @@ import {
   classifyReferenceRange,
   formatReferenceRange,
 } from "./labs/reference-range";
-import type { DoctorReportData } from "./doctor-report-data";
+import {
+  adherenceRatePercent,
+  type DoctorReportData,
+} from "./doctor-report-data";
 
 type T = (key: string, params?: Record<string, string | number>) => string;
 
@@ -333,10 +336,11 @@ function buildClinicalSummaryLines(
   const compEntries = Object.values(data.compliance);
   const totalDoses = compEntries.reduce((s, c) => s + c.total, 0);
   const totalTaken = compEntries.reduce((s, c) => s + c.taken, 0);
-  if (totalDoses > 0) {
+  const headlineRate = adherenceRatePercent(totalTaken, totalDoses);
+  if (headlineRate !== null) {
     lines.push(
       t("doctorReport.summaryAdherence", {
-        rate: num((totalTaken / totalDoses) * 100, 1),
+        rate: num(headlineRate, 0),
       }),
     );
   }
@@ -986,7 +990,9 @@ export function buildDoctorReportPdfDocument(
       // detail-page adherence %. The column is labelled "Expected" rather
       // than "Total" so the row stays internally coherent: Taken + Missed =
       // Expected, with Skipped shown alongside as informational.
-      const rate = c.total > 0 ? `${num((c.taken / c.total) * 100, 1)}%` : "—";
+      // Integer percent via the canonical rounding — matches the in-app card.
+      const ratePct = adherenceRatePercent(c.taken, c.total);
+      const rate = ratePct !== null ? `${num(ratePct, 0)}%` : "—";
       return [
         name,
         String(c.taken),
@@ -1090,13 +1096,16 @@ export function buildDoctorReportPdfDocument(
         );
         y += 5;
       }
-      if (med.compliance.total > 0) {
-        const rate = (med.compliance.taken / med.compliance.total) * 100;
+      const glp1Rate = adherenceRatePercent(
+        med.compliance.taken,
+        med.compliance.total,
+      );
+      if (glp1Rate !== null) {
         doc.text(
           t("doctorReport.glp1Compliance", {
             taken: med.compliance.taken,
             total: med.compliance.total,
-            rate: num(rate, 1),
+            rate: num(glp1Rate, 0),
           }),
           margin,
           y,

src/lib/fhir/__tests__/build-bundle.test.ts

@@ -412,8 +412,9 @@ describe("buildFhirDocumentBundle", () => {
     const adherence = observations.find((o) =>
       o.code.coding?.some((c) => c.code === "71799-1"),
     );
-    // PDF renders taken/total = 85/90 = 94.4%.
-    expect(adherence?.valueQuantity?.value).toBeCloseTo(94.4, 1);
+    // taken/total = 85/90 = 94.4% → rounded to the app's integer convention
+    // (PDF + FHIR now share one rounding source of truth).
+    expect(adherence?.valueQuantity?.value).toBe(94);
   });
 
   it("emits one MedicationStatement per active medication", () => {

src/lib/fhir/resources.ts

@@ -17,7 +17,10 @@
  * convention. All text is escaped plain text; never user-supplied HTML
  * (no markdown library, no `dangerouslySetInnerHTML`).
  */
-import type { DoctorReportData } from "@/lib/doctor-report-data";
+import {
+  adherenceRatePercent,
+  type DoctorReportData,
+} from "@/lib/doctor-report-data";
 import { resolveGlucoseUnit, convertGlucose } from "@/lib/glucose";
 import {
   LOINC_SYSTEM,
@@ -660,7 +663,9 @@ export function observationsFromReportData(
   for (const [name, comp] of Object.entries(data.compliance)) {
     if (comp.total <= 0) continue;
     obsSeq += 1;
-    const rate = Math.round((comp.taken / comp.total) * 1000) / 10;
+    // Integer percent — the one canonical rounding the app card + PDF use, so
+    // a clinician sees the same adherence figure on every surface.
+    const rate = adherenceRatePercent(comp.taken, comp.total) ?? 0;
     push({
       resourceType: "Observation",
       id: `obs-${obsSeq}`,