jinko-task-trial-data-scoping

Clinical Trial Data Scoping

Produce a registry candidate inventory, not extracted endpoint data. A registered outcome is not evidence that numeric results were posted.

Frame

Establish the condition, intervention or mechanism class, population, comparator, and purpose: endpoint availability, control-arm or natural-history evidence, dose/regimen context, safety, or development-landscape intelligence. Confirm any required status, phase, study type, posted-results requirement, and shortlist size. Do not impose a status filter silently.

Reuse the Entity Table from jinko-task-literature-search when available; otherwise build canonical_name, synonyms, mesh_term, related_entities, intent_groups (Data), and exclusions. Confirm consequential aliases and filters before network calls.

Search

1. Build distinct ClinicalTrials.gov angles from the relevant facets: intervention aliases, condition aliases, mechanism class, comparator or standard of care, and population/outcome. Prefer precise terms over one broad query.
2. Run scripts/clinical_trials.py once per angle with separate output files. Use --status, --phase, and --require-results only when required by the approved frame. The script owns API filtering, raw-response persistence, and normalized registry fields.
3. Run scripts/compile_trials.py over the angle outputs. It deduplicates by NCT ID, preserves query provenance, and ranks by angle count, posted-results availability, and record completeness.

This is one search pass. Broader mechanism, sponsor, country, site, or comparator queries are separate user-approved follow-ups.

Shortlist

Inspect each retained record against the stated purpose. Distinguish:

• registry-only design or recruitment metadata;
• posted ClinicalTrials.gov results;
• a publication linked to an NCT identifier.

Set verification_passed only when the record contains purpose-relevant signals, such as a matching population/intervention, specified outcome and timeframe, enrollment and eligibility, appropriate design, or the required results modules. State the observed signals in verification_note; do not infer numeric endpoint availability from hasResults alone.

Complete the fields in assets/shortlist-schema.json and run scripts/validate_shortlist.py before presenting shortlist.json. Trial records use intent_group = Data, an appropriate registry/results evidence_type, and nct_id as their primary identifier.

When associated publications are needed, pass selected NCT IDs to jinko-task-literature-search as <NCT_ID>[si] angles. Use jinko-task-extract-data-table only after a quantitative source has been identified and inspected.

Artifacts

• frame.json: approved scope, Entity Table, filters, and angle definitions;
• per-angle normalized JSON, raw API JSON, and table JSON;
• merged_trials.json: deterministic cross-angle candidate pool;
• shortlist.json: schema-valid prioritized candidates.

Present a concise Markdown view with NCT link, title, phase/status, results availability, population, interventions, primary outcomes, and priority rationale — cite any linked publication as Author (Year) [PMID], matching jinko-task-literature-search's convention. Clearly separate scoped candidates from analysis-ready data.