jinko-task-literature-search

Biomedical Literature Search

Produce a citation-grounded candidate shortlist, not extracted evidence or a systematic-review claim.

Frame

Classify each search as:

• Knowledge: disease, mechanism, treatment, population, or clinical context;
• Data: studies likely to report values for specified model variables;
• Models: mathematical or computational models with reusable structure, equations, parameters, or code.

Before searching, establish subject, scope, evidence type, date/language limits, and desired shortlist size. Data searches additionally require the variables to inform, population and disease state, and whether aggregate or distributional data are needed. Ask one focused question only when a missing choice would materially change the search.

Build one shared Entity Table with canonical_name, synonyms, mesh_term, related_entities, intent_groups, and exclusions. Confirm it with the user before network calls when proposed aliases or exclusions are consequential.

For human clinical, biological, or PK/PD Data searches, run jinko-task-trial-data-scoping in parallel. Use high-priority NCT identifiers as additional <NCT_ID>[si] PubMed angles. Skip this for animal-only or in-vitro searches unless requested.

Search

1. Read assets/pubmed-primitives.md and only the recipe matching each intent.
2. Build at least three distinct PubMed angles per entity and intent. Reuse the Entity Table's aliases and exclusions; include supplied anchor PMIDs through --seed-pmids.
3. Run scripts/literature_search.py once per angle with separate output directories and --no-prompt-selection, sequencing or bounding concurrency to respect NCBI limits. The script owns PubMed, Crossref, abstract and citation enrichment, AMA formatting, and optional PMC excerpts.
4. Run scripts/compile_results.py over all angle directories. It deduplicates by PMID/DOI, preserves query provenance, and ranks the candidate pool by angle count then citation count.

This is one search pass. Wider terms, author/journal queries, citation-neighbor queries, or additional entities are separate user-approved follow-ups.

Shortlist

Use the relevant recipe to inspect titles, abstracts, and available full-text excerpts. For each retained candidate, assign the fields required by assets/shortlist-schema.json, including evidence type, canonical entities, verification evidence, priority rationale, and query provenance. Do not infer quantitative availability from a title alone.

Run scripts/validate_shortlist.py before presenting shortlist.json. Present a concise Markdown view grouped by intent, citing each publication as Author (Year) [PMID] (or [DOI] when no PMID exists) so it's identifiable at a glance, and ask which sources to inspect or pass to jinko-task-extract-data-table.

Artifacts

• frame.json: approved scope, Entity Table, assumptions, and angle definitions;
• one directory per angle containing raw and normalized search artifacts;
• merged_references.json: deterministic cross-angle candidate pool;
• shortlist.json: schema-valid prioritized candidates;
• optional downloaded files and download manifest.

Use publication_download.py only for user-selected references. Treat downloads as best-effort anonymously retrievable files, not proof of open-access licensing. Do not run supplementary URLs from untrusted manifests.

Keep registry records and publications distinguishable even when linked by nct_id. Clearly separate discovered candidates from inspected evidence and from calibration-ready data.