Meta-Analysis Skill

You are helping a medical researcher conduct a systematic review and meta-analysis. You support the full pipeline from protocol development to submission-ready manuscript, with specialized support for diagnostic test accuracy (DTA) meta-analyses.

Communication Rules

• Communicate with the user in their preferred language.
• All output documents, code, and checklists in English.
• Medical terminology always in English.

Reference Files

Built-in References (

${CLAUDE_SKILL_DIR}/references/

)

• PROSPERO template:

  ${CLAUDE_SKILL_DIR}/references/PROSPERO_template.md

  -- field-by-field guide with word limits, pitfalls checklist
• ICMJE COI guide:

  ${CLAUDE_SKILL_DIR}/references/icmje_coi_guide.md

  -- batch generation, python-docx pitfalls, form structure
• R templates:

  ${CLAUDE_SKILL_DIR}/references/r_templates.md

• Checklists:

  ${CLAUDE_SKILL_DIR}/references/checklists/

  • PRISMA_DTA.md

    -- 27-item checklist

  • QUADAS2.md

    -- 4 domains + signalling questions

  • ROBINS_I.md

    -- 7 domains + pre-assessment + synthesis recommendation

  • RoB2.md

    -- 5 domains + signalling questions + overall judgment

  • PROBAST.md

    -- 4 domains + AI extension + validation studies

  • NOS.md

    -- Cohort (8 items) + Case-control (8 items) + star interpretation

  • JBI_Case_Series.md

    -- 10-item critical appraisal checklist for case series
• Phase 9 Co-author Circulation:

  ${CLAUDE_SKILL_DIR}/references/phase9_circulation.md

  -- thread continuity, attachment scope, recipient structure, 7-day window
• Phase 10 Self-Audit Recovery:

  ${CLAUDE_SKILL_DIR}/references/phase10_recovery.md

  -- trigger conditions, 12-step rebuild sprint, PROSPERO amendment, re-circulation framing
• Data integrity checklist:

  ${CLAUDE_SKILL_DIR}/references/data_integrity_checklist.md

  -- DI-1DI-9 extraction/synthesis guardrails (MA0103 empirical)
• Review orchestration:

  ${CLAUDE_SKILL_DIR}/references/review_orchestration.md

  -- RO-1~RO-5 circulation discipline (extends phase9_circulation.md)
• Submission package drift:

  ${CLAUDE_SKILL_DIR}/references/submission_package_drift.md

  -- multi-journal folder hygiene,

  DO_NOT_EDIT_HERE

  gate,

  _build.sh

  pattern
• Post-submission release ops:

  ${CLAUDE_SKILL_DIR}/references/post_submission_release_ops.md

  -- Zenodo DOI gating, tag-cleanup gates, reject-retarget versioning

Meta-Analysis Types

Auto-detect type from the research question or accept user specification.

Workflow Phases

Phase 1: Protocol Development

Goal: Produce a PROSPERO-ready protocol document.

1. Structure the research question:

   • DTA: PIRD (Population, Index test, Reference standard, Diagnosis)
   • Intervention: PICO (Population, Intervention, Comparator, Outcome)

2. Define eligibility criteria:

   • Study design (cross-sectional DTA, cohort, RCT, etc.)
   • Population characteristics
   • Index test / intervention specifics
   • Comparator / reference standard
   • Outcome measures (Se/Sp for DTA; effect size for intervention)
   • Exclusion criteria with justification

3. Plan the search:

   • Minimum 3 databases: PubMed, Embase, and Cochrane CENTRAL (add Scopus, Web of Science as needed)
   • Draft Boolean search strategy using PIRD/PICO components
   • Grey literature plan (conference abstracts, trial registries)
   • Language restrictions (state explicitly)
   • Date range with justification

4. Plan RoB assessment:

   • Select tool based on type (see table above)
   • State number of independent assessors (minimum 2)
   • Plan for disagreement resolution (consensus, third reviewer)

5. Plan synthesis:

   • DTA: bivariate random-effects model (Reitsma) or HSROC (Rutter & Gatsonis)
   • Intervention: random-effects (DerSimonian-Laird or REML)
   • Heterogeneity assessment plan
   • Subgroup / sensitivity analysis plan
   • Publication bias assessment plan

6. Generate PROSPERO registration document:

   • Read

     ${CLAUDE_SKILL_DIR}/references/PROSPERO_template.md

     for field-by-field guidance
   • Generate all fields with word counts (stay within limits per field)
   • Structure: title, review question, PICO, searches, data collection, outcomes, synthesis, subgroups, stage, affiliation
   • For mixed designs (comparative + single-arm): explicitly address comparator for both arms
   • For RoB: map tool to study design (NOS for comparative, JBI for case series → select "Other" in form)
   • Output: Markdown + DOCX (via pandoc) for copy-paste into PROSPERO web form
   • Append Common Pitfalls Checklist (HTML entities, word limits, stage constraint)
   • Save to project

     7_Submission/

     or equivalent directory

Phase 2: Search Strategy

Goal: Develop and validate reproducible search strategies.

1. Build search blocks from PIRD/PICO:

   • Population block (MeSH + free text)
   • Index test / Intervention block
   • Comparator / Reference standard block (optional)
   • Study design filter (if applicable)

2. Combine with Boolean operators:

   • Within blocks: OR
   • Between blocks: AND

3. Execute search per database using

   /search-lit

   :
   • PubMed: MeSH + free text
   • Embase: Emtree + free text
   • Additional databases as specified in protocol

4. Report search per PRISMA-S (Rethlefsen et al. 2021, PMID:33499930): Save search strategies as a structured document, one section per database, with date of search, number of results, and any limits applied.

5. Merge and deduplicate: Combine all database results into a single spreadsheet. Deduplicate by DOI first, then PMID. Save raw counts for PRISMA flow.

Phase 3: Screening & Selection

Goal: Systematic title/abstract and full-text screening with two independent reviewers.

3a. Round 1 — Initial Title/Abstract Screening (single reviewer)

1. Define exclusion codes from protocol (e.g., E1=Not target population, E2=Not intervention, E3=Ineligible type, E4=Non-human, E5=Duplicate).
2. For each record, screen title+abstract against eligibility criteria.
3. Mark each record as INCLUDE / EXCLUDE / MAYBE with reason code.
4. Output:

   round1_{date}.tsv

   with color-coded decisions.

3b. Round 2 — Dual Independent Title/Abstract Screening

1. A second independent reviewer (or AI as a documented second-pass tool with human verification) re-screens all R1 records.
2. Compute Cohen's kappa at title/abstract stage; report in Methods.
3. Tag each record's

   round2_tag

   as INCLUDE / EXCLUDE / MAYBE based on R1+R2 agreement (MAYBE = disagreement OR either reviewer flagged uncertain).
4. Output:

   round2_{date}.tsv

   (adds

   round2_tag

   ,

   round2_reason

   columns).

3c. Round 3 — Adjudication of Disagreements (first reviewer)

1. Build R3 sheet: all MAYBE records first, followed by INCLUDE records (which receive a brief confirmation pass).
2. The first reviewer independently adjudicates each row, recording

   round3_decision

   (INCLUDE/EXCLUDE) and

   round3_reason

   (only when overturning R2).
3. Optional AI-assisted pre-screening to compress R3 effort:
   • Use

     references/ai_pre_screening_template.py

     (customize per project).
   • Pre-screen produces

     ai_suggestion

     (INCLUDE/EXCLUDE/UNCERTAIN/CONFIRM-INCLUDE) +

     ai_reason

     columns.
   • Sort priority: UNCERTAIN → EXCLUDE → INCLUDE → CONFIRM-INCLUDE.
   • First reviewer must independently confirm or overturn every AI suggestion against the title, abstract, and (when needed) full text. AI suggestions are not final decisions.
   • Methods boilerplate: "Round 3 adjudication was performed by the first reviewer with AI-assisted pre-screening ({model name and version}). The AI was prompted with the prespecified PECOS criteria and produced a suggestion plus brief justification for each record; the first reviewer independently confirmed or overturned every suggestion. AI suggestions were not used as final inclusion decisions."
4. Output:

   round3_{date}.tsv

   with finalized

   round3_decision

   .

3d. Round 4 — Full-text Screening

1. For records with

   round3_decision = INCLUDE

   , retrieve full-text PDFs (use

   /fulltext-retrieval

   ).
2. Apply full-text exclusion criteria (F1=No extractable outcome, F2=No comparative data, F3=Cannot separate target population data, F4=Inadequate sample/follow-up, F5=Full-text unavailable).
3. Two independent reviewers; compute Cohen's kappa at full-text stage.
4. Resolve disagreements by consensus or third reviewer.
5. Flag comparative studies for priority extraction.

3e. PRISMA Flow

Track numbers at each stage for PRISMA flow diagram (R1 → R2 → R3 → R4 → final included). Use

/make-figures

3f. Post-Consensus Count Reconciliation Gate (MANDATORY before Phase 5 write-up)

Before handing the screening artifacts to Phase 5 (statistical synthesis) or to

/write-paper

/self-review

2_Screening/screening_consensus_final.md

Use the deterministic helper when TSV/CSV artifacts are available:

python "${CLAUDE_SKILL_DIR}/scripts/screening_reconcile.py" \
  --screening 2_Screening/fulltext_screening_final.tsv \
  --consensus 2_Screening/consensus_decisions.tsv \
  --table1 6_Tables/table1_studies.csv \
  --output 2_Screening/screening_consensus_final.json

Downstream stages should consume

screening_consensus_final.json

1. Enumerate ID sets from raw artifacts (not from prose summaries):

   • A = screening TSV INCLUDE IDs
   • B = consensus spreadsheet Exclude IDs
   • C = consensus spreadsheet Include-qualitative IDs (FLAG-resolved additions)
   • T = Table 1 / bivariate-eligible IDs (2×2-extractable studies)

2. Compute canonical totals via set algebra:

   • k_qualitative = |A \ B| + |C|
   • k_bivariate = |T|
   • k_narrative-only = k_qualitative − k_bivariate
   • k_FT-excluded = |full-text reviewed| − k_qualitative

3. List the narrative-only IDs explicitly. The highest-yield red flag is a numeric claim ("10 narrative-only studies") that does not match the enumerable ID set (A ∪ C) \ B \ T.

4. Prohibit "N → M" transitions without ID receipts. Any sentence of the form "k rose from 30 to 32 after FLAG consensus" must cite the specific added/removed IDs. A transition claim with no enumerable ID set is a P0 error and blocks the Phase 5 hand-off.

5. Record in a reconciliation table inside the screening-consensus document:

   Quantity	v_prev draft	v_current (ID-verified)	Derivation
   k_full-text	...	...	...
   k_FT-excluded	...	...
   k_qualitative	...	...
   k_bivariate	...	...
   k_narrative-only	...	... (explicit IDs listed)	(A ∪ C) \ B \ T

Precedent incident (CBCT Biopsy MA-1, 2026-04-20): v11 manuscript shipped with k_qualitative = 32 / k_narrative-only = 10 / k_FT-excluded = 46. ID-set reconciliation (only performed after Codex adversarial audit at post-Stage 4 QC) revealed true counts 24/2/54. The prose "30 → 32 after FLAG consensus" had been carried from v7 without ever being reconciled against

fulltext_screening_final.tsv

MA1_Consensus_Sheet.xlsx

Phase 4: Data Extraction

Goal: Create standardized extraction forms and extract 2x2 or effect size data.

Failure-mode cross-ref →

references/data_integrity_checklist.md

DI-1~DI-5 are mandatory during extraction (2x2 arm-swap, KM audit trail, methodology mismatch, PRISMA 5-way drift, single-source k).

DTA Meta-Analysis:

Generate a data extraction form with:

• Study ID (first author, year)
• Study characteristics (country, design, setting, enrollment period)
• Population (n, age, sex, disease prevalence)
• Index test details (technique, threshold, manufacturer, reader experience)
• Reference standard details
• 2x2 table (TP, FP, FN, TN)
• Additional outcomes (AUC per study, if reported)
• Notes on partial verification, differential verification, uninterpretable results

Intervention Meta-Analysis:

Generate a data extraction form with:

• Study ID
• Study characteristics
• Population
• Intervention / comparator details
• Outcome data (means, SDs, event counts, sample sizes)
• Effect measures (OR, RR, HR, MD, SMD as appropriate)

Output: Excel/CSV template for data entry.

4b. Special cases (KM reconstruction, composite exposure)

When studies report outcomes only as Kaplan-Meier curves (no raw event counts) or when the intervention is a composite of multiple techniques, load

${CLAUDE_SKILL_DIR}/references/phase4_km_composite.md

IPDfromKM

Data Extraction Cross-Verification

When comparing extraction results between independent reviewers (minimum 2), check:

0. Inter-reviewer agreement: Calculate and report screening agreement: % agreement or Cohen's kappa at title/abstract and full-text stages. If kappa was not calculated, report the exact number of discrepant records and the resolution method.

1. Denominator consistency: Verify sample sizes match between reviewers. Watch for per-patient vs per-lesion/per-tumor unit confusion. CRITICAL: The denominator may differ across outcomes within the same study (e.g., LTP assessed only among treatment-naive nodules, but complications assessed among all treated tumors). For each outcome, back-calculate:

   event ÷ denominator

   must equal the percentage reported in the paper's Tables. If it does not match, investigate the analysis population definition in the Methods section. If denominators differ, return to the original paper's Tables/Flow diagram.

2. Arithmetic verification: Back-calculate proportions from event/total counts and cross-check against original text (e.g., 78/91 = 85.7%).

3. Kaplan-Meier estimate distinction: KM curve estimates differ from raw event counts. Always record the data source (Table vs KM curve vs text) during extraction.

4. Discrepancy resolution: List all discrepancies → verify against original text → reach consensus → if consensus fails, use third reviewer. Log all consensus decisions in

   {project}/consensus_log.md

   .

5. Dataset lock: After resolving all discrepancies, lock the final dataset. Any subsequent changes require documented justification with date.

Phase 5: Risk of Bias Assessment

Goal: Guide structured RoB assessment with the appropriate tool.

Select tool based on meta-analysis type (see table above), then read the corresponding checklist:

${CLAUDE_SKILL_DIR}/references/checklists/QUADAS2.md

${CLAUDE_SKILL_DIR}/references/checklists/RoB2.md

${CLAUDE_SKILL_DIR}/references/checklists/ROBINS_I.md

${CLAUDE_SKILL_DIR}/references/checklists/PROBAST.md

${CLAUDE_SKILL_DIR}/references/checklists/NOS.md

${CLAUDE_SKILL_DIR}/references/checklists/JBI_Case_Series.md

For AI/ML prediction models, also apply PROBAST+AI extensions.

Output: Summary table + traffic light plot (use

/make-figures

Phase 6: Statistical Synthesis

Goal: Execute meta-analysis and generate publication-ready outputs.

Failure-mode cross-ref →

references/data_integrity_checklist.md

DI-6/DI-7/DI-9 are the consistency gate (CSV ↔ script ↔ prose; single-source k; 3-way numeric reconciliation before Stage 4).

IMPORTANT: Always use R for meta-analysis (packages:

meta

metafor

mada

${CLAUDE_SKILL_DIR}/references/r_templates.md

mada::reitsma()

meta::metagen()

meta::metabin()

metabin

metaprop

Load-on-demand: Read

${CLAUDE_SKILL_DIR}/references/phase6_statistical_synthesis.md

Phase 6b: Post-Analysis Source Fidelity Audit (MANDATORY)

Goal: Catch numerical hallucinations that survived the forward pipeline (CSV → .R → manuscript).

Precedent failure pattern — treat this as a lived near-miss, not hypothetical:

In a revision-era comparative meta-analysis, a safety outcome was reported as "3/45 vs 0/56, p=0.085." The primary-source Table actually recorded "0/45 vs 1/56, p=0.37" — direction reversed. The extraction CSV was correct; the R script's Fisher exact

matrix()

was hand-typed after a column in the source Table was misread. Internal consistency checks passed because every downstream artifact (Abstract, Discussion, Table, forest caption) echoed the same wrong number. The reversal was caught only on a second-pass audit with random extraction sampling against the primary paper.

Non-negotiable rules:

1. No hand-typed numerical matrices when a CSV exists.

   • Use

     read.csv(...)

     + subset / filter. Never copy a 2x2 table from a paper's Table into

     matrix(c(...), ...)

     by eye.
   • If hand entry is truly unavoidable (e.g., text-only extraction), the

     matrix

     ,

     c()

     , or

     data.frame

     line MUST carry a comment citing the exact CSV row + column OR the exact primary-source Table/Page coordinate. Example:

     # source: data_extraction_final.csv row <N> (<first-author> <year>), cols <event_arm1>=0, <event_arm2>=1
     # verified against primary source Table <X>, page <P>
     fisher.test(matrix(c(0, 45, 1, 55), nrow = 2, byrow = FALSE))
     

2. Comparative-arm subsets are a separate consensus-log row.

   • When one study's arm-specific values (e.g., one arm of a multi-arm study) are used in a comparative analysis while the full cohort of that study appears elsewhere,

     extraction_consensus_log.md

     must carry an explicit row for the arm-specific values. Pooled totals and arm-specific values MUST NOT share a row.

3. Random 3-claim back-check before closing Phase 6.

   • After the forest/funnel/subgroup outputs stabilize, randomly sample 3 numerical claims from the Results section of the draft manuscript and trace each back to (a) the R output log and (b) the original paper's Table/Figure.

   • Record the back-check as a small table in

     peer_review_<vN>_internal.md

     :

     Claim (manuscript line)	R output file:line	Primary source (paper, Table/Fig, page)	Match?

   • A single mismatch is a P0 blocker — do not advance to Phase 7 until resolved.

4. Revision-introduced numbers must be tagged.

   • Any new number added after v1 — including numbers produced by a new comparative / subgroup / sensitivity script — MUST be wrapped inline as

     [VERIFY-CSV]

     in the manuscript until the Phase 2.5a audit in

     /self-review

     clears it.

When this phase triggers: every time Phase 6 outputs change (first draft, revision, reviewer- requested re-analysis). Not optional on "minor" re-runs — the precedent reversal above occurred inside a "minor" revision-era re-analysis.

Phase 7: GRADE / Certainty of Evidence

Goal: Assess certainty of the body of evidence.

For DTA meta-analysis, apply GRADE-DTA framework:

1. Risk of bias (from QUADAS-2)
2. Indirectness (applicability concerns)
3. Inconsistency (heterogeneity)
4. Imprecision (wide CIs, small sample)
5. Publication bias

For intervention meta-analysis, apply standard GRADE.

Output: Summary of Findings table.

Phase 8: Reporting & Manuscript

Goal: Generate PRISMA-compliant manuscript sections.

Failure-mode cross-ref →

references/submission_package_drift.md

— apply the

_build.sh

pattern +

DO_NOT_EDIT_HERE

gate when staging multi-journal submission folders.

1. Check reporting compliance: Use

   /check-reporting

   with PRISMA-DTA or PRISMA 2020
2. Write manuscript: Use

   /write-paper

   with meta-analysis type selected
3. Figures: Use

   /make-figures

   for:
   • PRISMA flow diagram
   • Forest plots (paired for DTA)
   • SROC curve (DTA)
   • Funnel plot
   • RoB summary (traffic light plot)
4. Tables:
   • Characteristics of included studies
   • 2x2 data per study (DTA)
   • RoB assessment results
   • Summary of findings / GRADE table

Phase 9: Co-author Circulation

Goal: Standardized pre-submission circulation of the manuscript to co-authors and senior methodologist / reviewer, with a bounded review window and a controlled attachment scope.

Trigger: Phase 8 is complete, and the draft has cleared Phase 6b source-fidelity audit.

Summary: Reply to the prior-version email thread to preserve

In-Reply-To

Load-on-demand procedural detail (thread continuity, attachment scope rationale, size-to-method table, journal-undetermined framing, response-tracking log):

${CLAUDE_SKILL_DIR}/references/phase9_circulation.md

Failure-mode cross-ref →

references/review_orchestration.md

RO-1~RO-5 (dual-rating completeness, defensive-tone bias audit, response-matrix numeric tracking, 2nd-reviewer availability blocking).

Phase 10: Self-Audit Recovery (v{N} → v{N+1} sprint)

Goal: When an audit uncovers a structural data or protocol-application error, withdraw the current version, rebuild, and re-circulate with a transparent audit trail. Catching the error yourself before a journal reviewer does is the principal trust-building move in this phase.

Trigger conditions (any one):

Non-negotiable rule: if the trigger fires after Phase 9 circulation but before journal submission, withdraw the current version within 24 hours. Reviewer discovery is a strictly worse failure mode than self-withdrawal.

Sprint outline (12 steps): (10.1) audit log at

qc/audit_vN_to_vNplus1.md

[VERIFY-CSV]

/make-figures

Load-on-demand procedural detail (exact audit-log fields, delta-table template, amendment language template, re-circulation paragraph template, anti-pattern rationale):

${CLAUDE_SKILL_DIR}/references/phase10_recovery.md

Failure-mode cross-ref →

references/post_submission_release_ops.md

Gate 4 covers reject/revise Zenodo versioning, tag-cleanup gate, and re-target workflow (avoid "new version" misuse on re-target).

Failure Modes (MA01~03 empirical)

Failure patterns observed across MA01 RFA Adjunct / MA02 CBCT Biopsy / MA03 CBCT Ablation. Each topical reference extends the phase it cross-references above — consult alongside phase procedural docs, not in isolation.

references/data_integrity_checklist.md

phase9_circulation.md

references/review_orchestration.md

DO_NOT_EDIT_HERE

references/submission_package_drift.md

references/post_submission_release_ops.md

Automation hooks (invoke at the phase listed)

python3 ${CLAUDE_SKILL_DIR}/../../scripts/extraction_consensus_log_init.py --output 2_Data/extraction_consensus_log.md

python3 ${CLAUDE_SKILL_DIR}/../../scripts/prisma_5way_consistency.py --ssot prisma.yaml

bash ${CLAUDE_SKILL_DIR}/../../scripts/tag_cleanup_gate.sh

VERIFY-CSV

TODO

FIXME

XXX

7_Manuscript

supplement

SUBMISSION

--record

--verify

python3 ${CLAUDE_SKILL_DIR}/../../scripts/verify_package_integrity.py --record --journal <name>

--verify --journal <name>

SUBMISSION/{journal}/

DO_NOT_EDIT_HERE.md

All four scripts are repo-shipped as of 2026-04 (FOLLOWUPS P10). Non-zero exit = gate failure; resolve before proceeding to the next phase.

DTA-Specific Pitfalls (Always Check)

Small Study Considerations

When the number of included studies is small (< 10):

• Bivariate/HSROC model may not converge -- consider univariate random-effects as fallback
• Publication bias tests are underpowered -- state this limitation
• Subgroup/meta-regression analysis not recommended
• Wide prediction regions expected -- emphasize uncertainty in conclusions
• Consider narrative synthesis as alternative/complement

Skill Interactions

/search-lit

/analyze-stats

/make-figures

/check-reporting

/write-paper

/self-review

/gws

/handoff

/write-paper

Error Handling

• If study type is ambiguous (DTA vs intervention), ask user to clarify before proceeding.
• If fewer than 4 studies for DTA, warn that bivariate model may not converge.
• If data extraction is incomplete (missing 2x2 cells), suggest contacting authors or sensitivity analysis with imputed values.
• If PROSPERO ID is missing, flag as a limitation but continue.
• Always remind user: this is a methodological support tool; final decisions rest with the research team and ideally include a biostatistician/methodologist.

Anti-Hallucination

• Never fabricate variable names, dataset column names, or variable codings. If a variable mapping is uncertain, output

  [VERIFY: variable_name]

  and ask the user to confirm against the data dictionary.
• Never fabricate statistical results — no invented p-values, effect sizes, confidence intervals, or sample sizes. All numbers must come from executed code output.
• Never generate references from memory. Use

  /search-lit

  for all citations.
• If a function, package, or API does not exist or you are unsure, say so explicitly rather than guessing.