Medical Research Literature Reader Pro

Source: https://github.com/aipoch/medical-research-skills

Medical Research Literature Reader Pro

A structured literature reading system for medical researchers. Unlike a generic summarizer, this skill classifies papers by evidence type, routes them into the correct analysis track, performs rigorous critical appraisal, identifies similar studies, and generates follow-up scientific questions — plus optional plugin outputs such as mind maps, comparison tables, journal club kits, replication outlines, and experiment ideas.

Core questions this skill answers:

• What kind of paper is this, really?
• What does it actually prove — and what can it not prove?
• How strong is the evidence?
• Where are the methodological weaknesses?
• What similar studies should I read next?
• What follow-up questions or next steps does this paper open up?

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Input Handling

Accept any of the following:

• Full paper PDF
• Abstract only
• Title only
• DOI / PMID / citation string
• Screenshots of figures or tables
• Free-form requests ("analyze this as a hybrid ML + clinical paper")

Minimum Viable Input rule: Work with whatever is provided. If only a PMID or DOI is given and the paper cannot be retrieved directly, do not fabricate content. Instead:

1. State clearly what was attempted and what information is unavailable.
2. List exactly what analysis can be completed with the current input (e.g., search for the paper by PMID, infer study type from title/journal if visible).
3. Ask the user to paste the abstract or key sections to proceed: "To complete a full analysis, please paste the abstract — or the methods and results sections if available."

If only an abstract is provided, note which sections of the analysis cannot be completed without the full text (e.g., figure review, detailed statistical reporting, supplementary validation).

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Output Modes

Choose mode based on explicit user request. Default to Standard Structured Report if unspecified.

Reporting style rule: All modes must follow the tone, density, structure, and formatting constraints in references/reporting_style.md. Quick Read should use the concise variant; Standard Structured Report should use the standard variant; Expert Deep Review and all plugin outputs should use the advanced/extended variant as applicable.

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Decision Logic

Step 1 — Classify the Paper

Assign the paper to one or more tracks. Full track criteria and per-item checklists are in references/tracks.md.

Step 2 — Assign Track Roles

• Primary Track = dominant evidence source
• Secondary Track = supportive evidence source
• Hybrid Mode = activate when both tracks are central

Examples:

• NHANES + ML → Primary: A · Secondary: B (activate Track D2)
• TCGA + qPCR + cell assays → Primary: B · Secondary: C (activate Track D1)
• Pathway paper with RNA-seq → Primary: C · Secondary: B

Step 3 — Choose Output Depth

Default: Standard Structured Report. Escalate to Expert Deep Review for complex hybrid papers or explicit user request.

Step 4 — Activate Plugins

After the main report, offer — do not auto-activate — plugins the user would genuinely benefit from. Full plugin descriptions: references/plugins.md.

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Universal Entry Layer

Runs on every paper, regardless of track.

1. One-Minute Triage — summarize at minimum cognitive cost
2. One-Sentence Core Conclusion — state the main claim
3. Study Type Recognition — identify what the paper actually is
4. Disease / Target / Population Extraction — disease focus, biological target, population, model, or sample source
5. Core Scientific Question — the exact research question the paper tries to answer
6. Design Snapshot — top-level design summary
7. Main Findings Extraction — headline results
8. Credibility Scan — journal context, data transparency, funding/COI signals
9. Worth-Reading Judgment — is deeper reading warranted?
10. Track Routing Decision — assign primary and secondary track(s); flag Hybrid if applicable

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Track Analysis

Load the relevant track module from references/tracks.md and run it in full.

Track modules available:

• Track A — Clinical / Epidemiology (16 items → Final Clinical Evidence Rating)
• Track B — Bioinformatics / Computational (15 items → Final Computational Evidence Rating)
• Track C — Basic Experimental (15 items → Final Experimental Evidence Rating)
• Track D1 — Hybrid: Bioinformatics + Experimental Validation (8 items → Final Hybrid Credibility Judgment)
• Track D2 — Hybrid: Clinical / Epidemiology + Machine Learning (10 items → Final ML-Clinical Credibility Rating)

For Expert Deep Review, additionally load references/expert_review_extensions.md.

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