mirror of
https://github.com/MBombeck/HealthLog.git
synced 2026-11-12 22:26:13 +00:00
Marc-André Bombeck
e432b53e0d
A vital or symptom curve that settles, re-deviates mid-episode, and settles again now reports the FINAL sustained in-band run as the recovery return rather than the first. The earlier first-return rule locked onto an interim settle and under-reported the gap whenever a relapse followed. The change is monotone — a last return is always at or after the first — so no existing recovery number shifts; it only extends the gap for episodes that genuinely relapsed. The anchor-after-first-deviation guard is unchanged: an early in-band run-up is still never read as a return. The symptom-burden track honours relapse identically over logged days so the headline median never mixes first- and last-return semantics. Adds relapse, multiple-relapse, too-short-final-run, ends-out-of-band, and symptom-relapse engine fixtures plus a cross-episode integration fixture.
299 lines
9.8 KiB
TypeScript
299 lines
9.8 KiB
TypeScript
/**
|
||
* v1.18.1 P3 — illness retrospective summary aggregation.
|
||
*
|
||
* Asserts the count/byMonth/byType tallies, the CHRONIC_ONGOING gap exclusion,
|
||
* and the v1.18.9 signal-density gates: the "typical recovery gap" is WITHHELD
|
||
* unless enough episodes EACH clear the per-episode measurement floor AND the
|
||
* resulting median magnitude is non-trivial (a 0/±1-day gap is coincidental
|
||
* noise, not a finding).
|
||
*/
|
||
import { describe, expect, it } from "vitest";
|
||
import {
|
||
summarizeIllnessRetrospective,
|
||
MIN_EPISODES_FOR_TYPICAL_GAP,
|
||
MIN_GAP_MEASUREMENT_DAYS,
|
||
MIN_TYPICAL_GAP_MAGNITUDE_DAYS,
|
||
type RetrospectiveEpisode,
|
||
} from "../retrospective";
|
||
import { computeIllnessCorrelation, type VitalDayPoint } from "../correlation";
|
||
|
||
/** A baseline with a non-zero, stable MAD (mirrors the correlation suite). */
|
||
function jitteredBaseline(
|
||
center: number,
|
||
jitter: number,
|
||
days: number,
|
||
endDay: string,
|
||
): VitalDayPoint[] {
|
||
const out: VitalDayPoint[] = [];
|
||
let cursor = Date.parse(`${endDay}T00:00:00Z`);
|
||
const offsets = [-jitter, -jitter / 2, 0, jitter / 2, jitter];
|
||
for (let i = 0; i < days; i++) {
|
||
out.unshift({
|
||
day: new Date(cursor).toISOString().slice(0, 10),
|
||
mean: center,
|
||
});
|
||
cursor -= 24 * 60 * 60 * 1000;
|
||
}
|
||
return out.map((p, i) => ({
|
||
...p,
|
||
mean: center + offsets[i % offsets.length],
|
||
}));
|
||
}
|
||
|
||
function ep(over: Partial<RetrospectiveEpisode>): RetrospectiveEpisode {
|
||
return {
|
||
id: "e",
|
||
type: "INFECTION",
|
||
onsetDay: "2026-01-10",
|
||
resolved: true,
|
||
recoveryGapDays: 2,
|
||
// Default to a well-covered episode so the gap-magnitude / sample-count
|
||
// tests aren't masked by the per-episode measurement floor.
|
||
gapMeasurementDays: MIN_GAP_MEASUREMENT_DAYS + 2,
|
||
lifecycle: "ACUTE",
|
||
...over,
|
||
};
|
||
}
|
||
|
||
describe("summarizeIllnessRetrospective", () => {
|
||
it("counts episodes, resolved, by-month, by-type", () => {
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({ id: "a", onsetDay: "2026-01-05", type: "INFECTION" }),
|
||
ep({
|
||
id: "b",
|
||
onsetDay: "2026-11-20",
|
||
type: "INFECTION",
|
||
resolved: false,
|
||
recoveryGapDays: null,
|
||
}),
|
||
ep({ id: "c", onsetDay: "2026-11-02", type: "ALLERGY" }),
|
||
]);
|
||
expect(out.episodeCount).toBe(3);
|
||
expect(out.resolvedCount).toBe(2);
|
||
expect(out.byMonth[1]).toBe(1);
|
||
expect(out.byMonth[11]).toBe(2);
|
||
expect(out.byType.INFECTION).toBe(2);
|
||
expect(out.byType.ALLERGY).toBe(1);
|
||
});
|
||
|
||
it("withholds the typical gap below the min-sample floor", () => {
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({ id: "a", recoveryGapDays: 3 }),
|
||
ep({ id: "b", recoveryGapDays: 5 }),
|
||
]);
|
||
expect(MIN_EPISODES_FOR_TYPICAL_GAP).toBe(3);
|
||
expect(out.gapSampleSize).toBe(2);
|
||
expect(out.typicalRecoveryGapDays).toBeNull();
|
||
});
|
||
|
||
it("reports the median typical gap at/above the floor", () => {
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({ id: "a", recoveryGapDays: 2 }),
|
||
ep({ id: "b", recoveryGapDays: 4 }),
|
||
ep({ id: "c", recoveryGapDays: 6 }),
|
||
]);
|
||
expect(out.gapSampleSize).toBe(3);
|
||
expect(out.typicalRecoveryGapDays).toBe(4);
|
||
});
|
||
|
||
it("excludes CHRONIC_ONGOING and null gaps from the median", () => {
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({ id: "a", recoveryGapDays: 2 }),
|
||
ep({ id: "b", recoveryGapDays: 4 }),
|
||
ep({ id: "c", recoveryGapDays: 6 }),
|
||
ep({ id: "d", recoveryGapDays: 100, lifecycle: "CHRONIC_ONGOING" }),
|
||
ep({ id: "e", recoveryGapDays: null }),
|
||
]);
|
||
expect(out.gapSampleSize).toBe(3);
|
||
expect(out.typicalRecoveryGapDays).toBe(4);
|
||
});
|
||
|
||
it("drops episodes below the per-episode measurement floor", () => {
|
||
// Three computed gaps, but only one episode is well-covered — the other
|
||
// two ride on a couple of coincidental readings and must not count.
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({ id: "a", recoveryGapDays: 4, gapMeasurementDays: 8 }),
|
||
ep({
|
||
id: "b",
|
||
recoveryGapDays: 5,
|
||
gapMeasurementDays: MIN_GAP_MEASUREMENT_DAYS - 1,
|
||
}),
|
||
ep({ id: "c", recoveryGapDays: 6, gapMeasurementDays: 0 }),
|
||
]);
|
||
expect(out.gapSampleSize).toBe(1);
|
||
expect(out.typicalRecoveryGapDays).toBeNull();
|
||
});
|
||
|
||
it("treats a missing gapMeasurementDays as non-qualifying", () => {
|
||
const bare = (id: string): RetrospectiveEpisode => ({
|
||
id,
|
||
type: "INFECTION",
|
||
onsetDay: "2026-01-10",
|
||
resolved: true,
|
||
recoveryGapDays: 4,
|
||
lifecycle: "ACUTE",
|
||
});
|
||
const out = summarizeIllnessRetrospective([
|
||
bare("a"),
|
||
bare("b"),
|
||
bare("c"),
|
||
]);
|
||
expect(out.gapSampleSize).toBe(0);
|
||
expect(out.typicalRecoveryGapDays).toBeNull();
|
||
});
|
||
|
||
it("withholds a trivial (sub-threshold) median gap as noise", () => {
|
||
// Enough well-covered episodes, but the gaps cluster at 0/±1 — the
|
||
// coincidental-data baseline. No speculative gap is surfaced.
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({ id: "a", recoveryGapDays: 0 }),
|
||
ep({ id: "b", recoveryGapDays: 1 }),
|
||
ep({ id: "c", recoveryGapDays: -1 }),
|
||
]);
|
||
expect(MIN_TYPICAL_GAP_MAGNITUDE_DAYS).toBe(2);
|
||
expect(out.gapSampleSize).toBe(3); // they qualified on coverage…
|
||
expect(out.typicalRecoveryGapDays).toBeNull(); // …but the magnitude is noise
|
||
});
|
||
|
||
it("names the dominant driving vital when a gap is surfaced", () => {
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({
|
||
id: "a",
|
||
recoveryGapDays: 4,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
ep({
|
||
id: "b",
|
||
recoveryGapDays: 6,
|
||
gapReturnTypes: ["RESTING_HEART_RATE", "HEART_RATE_VARIABILITY"],
|
||
}),
|
||
ep({
|
||
id: "c",
|
||
recoveryGapDays: 5,
|
||
gapReturnTypes: ["HEART_RATE_VARIABILITY"],
|
||
}),
|
||
]);
|
||
// RHR returned in 2 of 3 episodes, HRV in 2 as well — tie → deterministic
|
||
// alphabetical resolution picks HEART_RATE_VARIABILITY.
|
||
expect(out.typicalRecoveryGapDays).toBe(5);
|
||
expect(out.gapDriverType).toBe("HEART_RATE_VARIABILITY");
|
||
});
|
||
|
||
it("picks the strictly-most-frequent driver", () => {
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({
|
||
id: "a",
|
||
recoveryGapDays: 4,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
ep({
|
||
id: "b",
|
||
recoveryGapDays: 6,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
ep({
|
||
id: "c",
|
||
recoveryGapDays: 5,
|
||
gapReturnTypes: ["HEART_RATE_VARIABILITY"],
|
||
}),
|
||
]);
|
||
expect(out.gapDriverType).toBe("RESTING_HEART_RATE");
|
||
});
|
||
|
||
it("leaves the driver null when no gap is surfaced", () => {
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({
|
||
id: "a",
|
||
recoveryGapDays: 0,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
ep({
|
||
id: "b",
|
||
recoveryGapDays: 1,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
ep({
|
||
id: "c",
|
||
recoveryGapDays: -1,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
]);
|
||
// Magnitude gate withholds the gap → no driver is named.
|
||
expect(out.typicalRecoveryGapDays).toBeNull();
|
||
expect(out.gapDriverType).toBeNull();
|
||
});
|
||
|
||
it("surfaces a negative gap once its magnitude clears the floor", () => {
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({ id: "a", recoveryGapDays: -2 }),
|
||
ep({ id: "b", recoveryGapDays: -3 }),
|
||
ep({ id: "c", recoveryGapDays: -4 }),
|
||
]);
|
||
expect(out.typicalRecoveryGapDays).toBe(-3);
|
||
});
|
||
|
||
it("threads the larger last-return gap through the cross-episode median", () => {
|
||
// Integration guard for the relapse-aware (last-sustained) return change:
|
||
// one episode relapses mid-course, so its ENGINE-computed gap grows from
|
||
// the old first-return value (−1) to the last-return value (+6). The
|
||
// retrospective consumes that engine number verbatim — confirm the larger
|
||
// value pulls the cross-episode median up rather than the stale first
|
||
// return. No retrospective code changes; this guards the integration.
|
||
const baselineDays = jitteredBaseline(55, 1, 21, "2026-01-02");
|
||
const relapseEpisode: VitalDayPoint[] = [
|
||
{ day: "2026-01-10", mean: 75 }, // first deviation
|
||
{ day: "2026-01-13", mean: 55 }, // first settle (old return)
|
||
{ day: "2026-01-14", mean: 55 },
|
||
{ day: "2026-01-15", mean: 55 }, // 3-day run
|
||
{ day: "2026-01-16", mean: 75 }, // RELAPSE
|
||
{ day: "2026-01-18", mean: 73 },
|
||
{ day: "2026-01-20", mean: 55 }, // final settle start
|
||
{ day: "2026-01-21", mean: 55 },
|
||
{ day: "2026-01-22", mean: 55 }, // holds to end
|
||
];
|
||
const engine = computeIllnessCorrelation({
|
||
episodeId: "relapse",
|
||
window: {
|
||
onsetDay: "2026-01-10",
|
||
feltBetterDay: "2026-01-14",
|
||
lifecycle: "ACUTE",
|
||
},
|
||
series: [
|
||
{
|
||
type: "RESTING_HEART_RATE",
|
||
baselineDays,
|
||
episodeDays: relapseEpisode,
|
||
},
|
||
],
|
||
source: "DAY",
|
||
now: new Date("2026-02-01T00:00:00Z"),
|
||
});
|
||
expect(engine.status).toBe("ok");
|
||
if (engine.status !== "ok") return;
|
||
// Last-return = 01-20, gap = +6 (not the old first-return −1).
|
||
expect(engine.value.recoveryGapDays).toBe(6);
|
||
|
||
const out = summarizeIllnessRetrospective([
|
||
ep({
|
||
id: "relapse",
|
||
recoveryGapDays: engine.value.recoveryGapDays,
|
||
gapMeasurementDays: engine.coverage.historyDays,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
ep({
|
||
id: "b",
|
||
recoveryGapDays: 4,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
ep({
|
||
id: "c",
|
||
recoveryGapDays: 5,
|
||
gapReturnTypes: ["RESTING_HEART_RATE"],
|
||
}),
|
||
]);
|
||
// Median of [6, 4, 5] = 5 — the relapse episode's grown gap is in the mix.
|
||
expect(out.typicalRecoveryGapDays).toBe(5);
|
||
expect(out.gapDriverType).toBe("RESTING_HEART_RATE");
|
||
});
|
||
});
|