Secondary Condition Finder
A claim-preparation research aid for VA disability claims. It keeps secondary
and presumptive service-connection research separate because they use different
legal and evidentiary frameworks.
- Secondary (38 CFR § 3.310, M21-1 Spicer update May 2026): starts from established service-connected
conditions and organizes diagnosed conditions that may warrant further
research, along with authored pathways, evidence gaps, and source references.
After the Spicer v. McDonough ruling, the M21-1 uses "but for" causation language
and recognizes treatment-interference as a secondary aggravation pathway.
Competent nexus evidence may be needed when the relationship requires medical
expertise; the app does not create or replace a medical opinion.
- Presumptive (38 CFR §§ 3.307 and 3.309): compares authored qualifying
service locations/date windows with the service history supplied in the
workflow and lists presumptions that may apply. A presumption can remove the
need to prove that qualifying service caused the recognized condition, but
the app does not determine eligibility.
The current presumptive data covers the authored PACT Act burn-pit and Agent
Orange categories described in docs/presumptive-track.md. Data-governance
rules for condition IDs, schema strictness, and citation quality are in
docs/data-governance-note.md.
This is not medical or legal advice. It does not diagnose conditions,
determine eligibility, or estimate ratings. Every suggestion is a possibility
to research with a qualified provider or an accredited representative.
Current status
Deployment
The application is deployed on Vercel. The canonical production URL is
https://secondary-condition-finder.vercel.app.
Knowledge-base snapshot — generated, not documented
The knowledge base changes with every authoring commit, so this README does not
hardcode snapshot counts (mappings, relationships, published/draft split,
verified references). Each was a recurring source of documentation-only
commits and merge conflicts. Generate the current numbers instead (the table below is generated from the snapshotGenerators metadata in package.json by npm run readme:snapshot — edit the metadata, not the table):
| Snapshot metric | Generator |
|---|
| Catalog + mapping integrity, file/condition counts | npm run kb:validate |
| Citation verification state and the verification-debt ratchet | npm run citations:audit |
| Published/draft authoring coverage by wave and batch | npm run authoring:tracker |
| References below the provenance bar | npm run citations:weak |
| CRG relationship-graph structure (cycles, bidirectional pairs, redundancy) | npm run audit:crg |
| CRG transitive-redundancy triage register and posture tables | npm run crg:triage |
| Qualified-human citation-verification queue | npm run citations:queue |
Browser-local evidence processing
Supported PDF, DOCX, and text files are read entirely in the browser tab —
raw bytes and extracted text are never submitted to the application server.
- Step 6 groups excerpts under each authored claim pathway in a
Pathway Evidence Mapping table (research cues only — no interpretation,
no egress), alongside per-gap coverage chips and the evidence checklist.
- Step 4 offers two analysis depths — Standard (the default) and an
opt-in Extended envelope for large local sets such as full C-files — with
file, batch, page, character, and retention limits derived from one limits
module and published verbatim on
/privacy.
- Each document carries its own author credential (physician, psychologist,
NP/PA, non-clinician, or unknown), so VA decision letters are not counted as
clinician-authored medical opinions.
- The classifier understands clinical shorthand (
dx, 2/2 = secondary to),
normalizes hyphen/spelling variants, treats Diagnosis:/Assessment:
sections as diagnosis context, and excludes regulatory boilerplate from
nexus counts.
- Scanned or image-only PDFs are recognized with Local OCR that runs
entirely in the tab against same-origin tesseract.js assets.
- Every boundary is enforced before or during extraction, including DOCX
archive-expansion preflight bounds.
Guided VA.gov records walkthrough
Step 4 includes a guided VA.gov medical-records download walkthrough for
veterans who want their complete VA health record. Downloading it is a
multi-page journey — VA.gov signs you in through ID.me (email, password, and a
multi-factor code), then the download tool asks for a date range, record type,
and file type before producing the PDF.
- The walkthrough mirrors that flow as a 14-step checklist with deep links
that open VA.gov in a new tab.
- It never collects, transmits, or stores VA.gov or ID.me credentials, MFA
codes, or session data — sign-in happens entirely on VA.gov.
- Progress checkmarks live only in the current browser tab.
- It appears on the secondary workflow's document step and standalone at
/get-records.
The screenshot is regenerable with node scripts/capture-va-gov-walkthrough-screens.mjs (starts a dev server and
headless Chrome, seeds the browser-local workflow record in sessionStorage,
and captures the card via the DevTools Protocol).
Step 5–6 research aids
Steps 5 and 6 surface research aids built from the same authored data:
This is the beginning of the README. Read the rest at the original. Read the full README ↗
