meta-analysis

name	meta-analysis
description	Systematic review and meta-analysis pipeline for medical research. Covers protocol registration (PROSPERO), search strategy, screening, data extraction, risk of bias assessment (QUADAS-2/ROBINS-I), statistical synthesis (bivariate/HSROC for DTA, random-effects for intervention), and PRISMA-compliant reporting. Supports both DTA and intervention meta-analyses.
triggers	meta-analysis, systematic review, PROSPERO, QUADAS-3, forest plot, funnel plot, PRISMA, QUADAS, ROBINS, HSROC, bivariate model, pooled sensitivity, pooled specificity, search strategy, study selection, data extraction form
tools	Read, Write, Edit, Bash, Grep, Glob
model	inherit

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
  • QUADAS3.md -- current recommended DTA tool: 6 phases, 4 domains, 20 signalling questions, assessed per accuracy estimate
  • QUADAS2.md -- the 2011 tool: 4 domains + 10 signalling questions (use when appraising or reproducing a review that used it)
  • 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-1~DI-9 extraction/synthesis guardrails (prior anonymized MA projects)
• 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
• Empirical peer-review lessons: ${CLAUDE_SKILL_DIR}/references/empirical_lessons.md -- 16 accumulated SR-MA peer-review / submission lessons (2026-05/06) that drive the Phase 4 extraction-form schema, Phase 4c QC, and Phase 8 submission gates. Load before designing the extraction form and before submission.

Built-in Templates (${CLAUDE_SKILL_DIR}/templates/)

• Extraction Form v2 (templates/extraction_form_v2.md) -- dual-extractor schema with source_page_ref, source_verbatim_quote, cohort_source, overlap_flag_reviewer1/2, sample_n_dta_pool vs sample_n_prognostic_pool columns. Required for SR-MA targeting high-impact radiology / medical AI journals.
• Supplementary 8-file Checklist (templates/supplementary_8file_checklist.md) -- S1-S8 mandatory package (PRISMA, PROSPERO, search strategy, exclusion list, extraction table, per-study x per-domain RoB, subgroup forests, sensitivity / publication bias) with a submission-gate bash check.

Built-in Scripts (${CLAUDE_SKILL_DIR}/scripts/)

• screening_reconcile.py -- Phase 3f ID-set screening reconciliation.
• check_pool_consistency.py -- pool-composition / PRISMA count consistency.
• cohort_overlap_check.py -- shared-database cohort-overlap detection.
• extract_assist.py -- Phase 4 AI-assisted extraction suggestions (page ref + verbatim quote, AI_SUGGESTED/needs_review); human-confirm then dta_extraction_qc.py. Challenge card: scripts/extract_assist_challenge/.
• dta_extraction_qc.py -- 2x2 cell ↔ source sens/spec QC on the confirmed extraction CSV.

---

Meta-Analysis Types

Type	RoB Tool	Statistical Model	Reporting Guideline
DTA (diagnostic test accuracy)	QUADAS-3 (QUADAS-2 for legacy reviews)	Bivariate / HSROC	PRISMA-DTA
Intervention (treatment effect)	RoB 2 (RCT) / ROBINS-I (NRSI)	Random-effects (DL/REML)	PRISMA 2020
Prognostic (prediction model)	QUIPS / PROBAST	Random-effects	PRISMA 2020
Observational (prevalence/association)	NOS / JBI	Random-effects	MOOSE

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. DTA only — do QUADAS-3 phases 1 and 2 now, not at risk-of-bias time: QUADAS-3's first two phases are review-level and belong in the protocol: phase 1 states the synthesis question(s) (population, index test(s), target condition — a review may have more than one), and phase 2 defines the ideal test accuracy trial for each: objective, participants, index test(s), definition of the target condition, analysis. Every later risk-of-bias and applicability judgement is made against that trial. Write the review-specific guidance for answering each signalling question here too, with clinical and methodological input, and publish it as a web appendix. Defining the ideal trial after seeing the studies is not an assessment — it is a judgement fitted to the results. See references/checklists/QUADAS3.md.

3. 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

4. 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

5. 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)

6. 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

7. Generate PROSPERO registration document:

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). Define the exclusion codes from the protocol (E1=Not target population, E2=Not intervention, E3=Ineligible type, E4=Non-human, E5=Duplicate). Mark every record INCLUDE / EXCLUDE / MAYBE with a reason code → round1_{date}.tsv.

3b. Round 2 — dual independent title/abstract screening. A second independent reviewer (or AI as a documented second-pass tool with human verification) re-screens all R1 records. Compute Cohen's κ and report it in Methods. round2_tag = INCLUDE / EXCLUDE / MAYBE, where MAYBE means disagreement or either reviewer flagged uncertainty → round2_tag, round2_reason columns.

3c. Round 3 — adjudication of disagreements (first reviewer). Build the R3 sheet with all MAYBE records first, then INCLUDE records for a brief confirmation pass. The first reviewer independently adjudicates each row (round3_decision, plus round3_reason only when overturning R2). Optional AI-assisted pre-screening can compress the effort — but AI suggestions are not decisions: the reviewer independently confirms or overturns every one. Template, sort priority, and the required Methods boilerplate are in the reference file.

3d. Round 4 — full-text screening. Retrieve full texts for round3_decision = INCLUDE (use /fulltext-retrieval), apply the full-text exclusion codes (F1=No extractable outcome, F2=No comparative data, F3=Cannot separate target population, F4=Inadequate sample/follow-up, F5=Full-text unavailable), with two independent reviewers, Cohen's κ, and consensus or a third reviewer for disagreements. Flag comparative studies for priority extraction.

3e. PRISMA flow. Track counts at every stage (R1 → R2 → R3 → R4 → final included); generate the diagram with /make-figures once the numbers are final.

3f. Post-consensus count reconciliation gate (MANDATORY before Phase 5 write-up). Reconcile the counts from the raw ID sets, never from prose summaries, and record the canonical totals in one source-of-truth file:

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

Downstream stages consume screening_consensus.json for counts and ID sets; the Markdown consensus document remains the human explanation. Three hard rules:

1. 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 set (A ∪ C) \ B \ T.
2. No "N → M" transition without ID receipts. "k rose from 30 to 32 after FLAG consensus" must cite the added/removed IDs. A transition claim with no enumerable ID set is a P0 and blocks the Phase 5 hand-off.
3. STAGE_TRANSFER_LOSS is a P0. Exit 1 when a record is included at screening but absent from the consensus artifact altogether — no adjudication was ever recorded. An exclusion is a decision; silence is a gap. Never let it settle into narrative-only (why: reference file).

The set algebra, the reconciliation-table template, and the precedent (a manuscript shipped 32/10/46 where the ID sets said 24/2/54, with four artifacts echoing the same unreconciled prose total) are in the reference file.

3f.5 Pool composition lock (MANDATORY at adjudication freeze). Once 3f passes, freeze the pool into a single source-of-truth YAML that every downstream artifact can be checked against:

cp "${CLAUDE_SKILL_DIR}/templates/FINAL_POOL_LOCK.yaml.template" 2_Data/FINAL_POOL_LOCK.yaml
# fill counts + UID lists from 3f, compute the SHA-256 over the sorted UID list,
# and COMMIT THE LOCK before any Phase 4 extraction

• Never re-derive k included from the extraction TSV at manuscript build time — always reference final_pool_n from the lock.
• Aggregate patient/lesion totals are locked too, not just study counts. Distinguish arm-separable from both-arm rows: a study contributing one arm must not have its full-cohort count folded into a pooled total. A hand-carried headline total that does not re-derive from the locked per-study values is a P0.
• A late post-freeze change to the pool is a formal PROSPERO amendment: file it, re-freeze as FINAL_POOL_LOCK_v2.yaml, and propagate to every artifact.

Read on demand:

File	Read it when	Cost if read blindly
references/phase3_screening_detail.md	you are executing a screening round, using AI pre-screening, or a reconciliation/lock gate fired	~3,600 tokens; the round procedures are needed one round at a time, not all at invocation

Phase 4: Data Extraction

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

4.0 Entry gate (MANDATORY) — pool composition lock ↔ adjudication TSV. Before any extraction work begins, confirm the round-3 adjudication TSV and FINAL_POOL_LOCK.yaml (Phase 3f.5) agree on which UIDs are included:

python "${CLAUDE_SKILL_DIR}/scripts/check_pool_consistency.py" \
    --lock 2_Data/FINAL_POOL_LOCK.yaml \
    --adjudication-tsv 2_Screening/round3_adjudication.tsv \
    --decision-col round3_decision --uid-col uid \
    --include-labels "INCLUDE,INCLUDE_MIXED" \
    --out qc/pool_consistency.json

The gate fails closed: any UID disagreement blocks extraction. Resolve by re-freezing the lock with the corrected UID set (and propagating downstream) or by correcting a mis-labelled TSV row. Do NOT proceed with a mismatch — the extraction matrix will not align with the locked pool, and the drift surfaces as a fabrication-grade red flag at peer review.

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).

Extraction form. For an SR-MA targeting high-impact radiology / medical AI journals use ${CLAUDE_SKILL_DIR}/templates/extraction_form_v2.md — its dual-extractor, source-page-reference, and verbatim-quote columns are what close the 2x2 cell-swap and cohort-overlap blind spots. The DTA and intervention field lists are in the reference file.

AI-drafted starting document — treat as hallucination-suspect. If a mentor or collaborator shared an AI-drafted study list, 2x2 set, or effect estimates (even flagged "for reference only"): save it with a _DO_NOT_USE_VERBATIM suffix and re-verify every N, denominator, event count, OR/CI, and author/year against the source PDF. Trust hierarchy: source PDF + own analysis stdout > the mentor's direct text > the attached AI draft — never promote a draft up that ladder. Procedure and precedent: reference file.

4b. Special cases (KM reconstruction, composite exposure). When studies report outcomes only as Kaplan-Meier curves, or the intervention is a composite of techniques, load ${CLAUDE_SKILL_DIR}/references/phase4_km_composite.md for the WebPlotDigitizer → IPDfromKM procedure (cite Guyot et al. 2012, doi:10.1186/1471-2288-12-9) and the 4-path composite-exposure decision tree. Pre-specify a sensitivity analysis excluding composite-exposure studies.

Cross-verification (≥2 independent reviewers). Report inter-reviewer agreement (% or Cohen's κ) at title/abstract and full-text stages. Verify denominator consistency — the denominator may differ across outcomes within one study, so for each outcome back-calculate event ÷ denominator and confirm it reproduces the paper's reported percentage. Distinguish KM-curve estimates from raw event counts and record the data source (Table / KM / text). Log every consensus decision in {project}/consensus_log.md, then lock the dataset; later changes need a dated justification.

4c. Extraction QC & cohort overlap. After dual-extractor consensus, run both before locking:

# 2x2 cell integrity: validates TP/FN/TN/FP against source-reported sens/spec (catches arm-swap)
python3 "${CLAUDE_SKILL_DIR}/scripts/dta_extraction_qc.py" \
  --input 2_Extraction/extraction.csv --tolerance 0.02 \
  --out 2_Extraction/qc/dta_extraction_qc.tsv

# cohort overlap: shared public DB / same institution+period / same first author ±2y
python3 "${CLAUDE_SKILL_DIR}/scripts/cohort_overlap_check.py" \
  --input 2_Extraction/studies.csv --enrich \
  --out 2_Extraction/qc/cohort_overlap.md

Any FLAG_SWAP / FLAG_MISMATCH requires third-reviewer adjudication before Phase 6. A confirmed flag is not resolved until the extraction form itself is edited — a flag corrected only in a review note silently re-enters synthesis, so re-run the QC and confirm zero open flags before locking. HIGH-confidence overlap pairs require a Limitations acknowledgment plus a sensitivity analysis excluding one of the pair. Cross-links: /peer-review Phase 2A P1 + P2.

Read on demand:

File	Read it when	Cost if read blindly
references/phase4_extraction_detail.md	building the extraction form, an AI draft was shared, you want the optional extract_assist.py scaffolding, or a QC flag fired	~4,700 tokens; a clean dual-extraction with no AI draft needs none of it
references/phase4_km_composite.md	studies report only KM curves, or the exposure is composite	~2,200 tokens

Phase 5: Risk of Bias Assessment

Goal: Guide structured RoB assessment with the appropriate tool.

DTA: this phase runs QUADAS-3 phases 3–6 (flow diagram, identify the estimates to assess, assess, overall judgement). Phases 1–2 — the synthesis question and the ideal test accuracy trial — were written in Phase 1 above. If they were not, stop and write them before judging anything; they are the comparator every judgement is made against.

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

Tool	Checklist File
QUADAS-3 (DTA, current)	${CLAUDE_SKILL_DIR}/references/checklists/QUADAS3.md
QUADAS-2 (DTA, legacy)	${CLAUDE_SKILL_DIR}/references/checklists/QUADAS2.md
RoB 2 (RCT)	${CLAUDE_SKILL_DIR}/references/checklists/RoB2.md
ROBINS-I (NRSI)	${CLAUDE_SKILL_DIR}/references/checklists/ROBINS_I.md
PROBAST (Prediction)	${CLAUDE_SKILL_DIR}/references/checklists/PROBAST.md
NOS (Observational)	${CLAUDE_SKILL_DIR}/references/checklists/NOS.md
JBI (Case Series)	${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). See ${CLAUDE_SKILL_DIR}/references/r_templates.md for full code templates.

Analysis family	Primary tool	Key output
DTA	mada::reitsma() (bivariate)	Pooled Se/Sp + SROC with confidence/prediction regions
Intervention	meta::metagen() / meta::metabin()	Pooled OR/RR, I², Egger's test, leave-one-out
Dual (comparative + single-arm)	metabin + metaprop	PRIMARY vs SECONDARY per pre-specified protocol

Load-on-demand: Read ${CLAUDE_SKILL_DIR}/references/phase6_statistical_synthesis.md for the full R code templates, the dual-approach decision table (comparative vs single-arm), practical cautions (method.tau, HK CI, zero-cell correction), publication-bias test power, sensitivity-analysis menu, and error-handling rules.

Three checks before the pool is written up — each is a Methods sentence, not only a setting. R and detail in the same reference:

1. Is the event rare? A pooled event rate < 1%, or any zero-event arm, moves the analysis off the inverse-variance default onto Peto / Mantel-Haenszel without a zero-cell correction / GLMM. Inverse-variance methods including DerSimonian-Laird are to be avoided for rare events, and so are 0.5 continuity corrections with them.
2. Why this model? Fixed vs random is a judgment about whether one common true effect exists — never derived from Cochran's Q or I². "A random-effects model was used because I² was 65%" is a reviewer catch, not a rationale.
3. Does one study contribute several correlated effect sizes? Multiple outcomes, readers, thresholds, or time points from the same participants need one pre-specified estimate per study, a multivariate model, or robust variance estimation — not independent pooling.

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)

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-3, or QUADAS-2 for a legacy review)
2. Indirectness (applicability concerns)
3. Inconsistency (heterogeneity)
4. Imprecision (wide CIs, small sample)
5. Publication bias

For intervention meta-analysis, apply standard GRADE.

Certainty is assessed per outcome, not once for the review. The five domains resolve differently for each outcome — an outcome pooled from 12 studies with narrow CIs and one pooled from 3 with a wide CI do not share a rating, and a single review-level "moderate certainty" sentence tells a reader nothing about the outcome they came for. Rate every outcome carried into the Summary of Findings table, and state the reason for each downgrade (which domain, why) rather than the resulting label alone.

Output: Summary of Findings table — one row per outcome, carrying the pooled estimate with its precision alongside the certainty rating (high / moderate / low / very low).

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, then run it a second time over the abstract with PRISMA 2020 for Abstracts — 12 items, its own denominator. One run does not cover both.

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 (one row per outcome — Phase 7)

5. The items published radiology SR/MA most often drop. Park 2022 (Korean J Radiol; PMID:35213097) scored 24 SR/MAs against PRISMA 2020 and found 24 of 42 items reported by fewer than 80%. The checklist itself lives in /check-reporting; what follows is where drafts actually fail, so check these by hand before the compliance run rather than after it:

   PRISMA item	What is missing	Observed
   20a	For each synthesis, a brief summary of the contributing studies' characteristics and risk of bias — not one global paragraph covering all pools	0/24
   27	Data availability: which of the extraction forms, extracted data, analysis dataset, and analytic code are public, and where	0/24
   24a–c	Registration number, where the protocol can be read, and any amendment — an explicit "not registered" satisfies 24a	0/24
   22 / 15	Certainty of evidence per outcome, and the method used to assess it	9%
   13f / 20d	Sensitivity analysis: method and result	28%
   18	Risk of bias per study, shown study-by-study rather than as a pooled proportion	32%

---

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 continuity (v1 → v2 → v3 tracked in one place). Attach the manuscript body with figures inline and, for v≥2, a change summary — exclude graphical abstract, cover letter, COI forms, and supplementary until the target journal is confirmed. TO = corresponding author + one senior methodologist; CC = remaining co-authors. Set a 7-day deadline (5 business days + weekend). Ask the corresponding author for target-journal preference, reviewer candidates, and cover-letter framing.

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):

#	Trigger	Source
T1	Extraction CSV ↔ primary source disagreement for a cell feeding a pooled/subgroup estimate or reported proportion	Phase 6b audit
T2	Included/excluded study violates the pre-specified criteria on re-read	Protocol review
T3	Hand-typed numerical literal in the analysis script traces to a wrong value	Phase 6b audit
T4	PROSPERO protocol ↔ delivered analysis disagreement on outcome, subgroup, or eligibility	Protocol ↔ analysis diff
T5	Dual-reviewer consensus record ↔ locked dataset disagreement on inclusion	Consensus log diff

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 → (10.2) CSV re-verification with [VERIFY-CSV] tagging → (10.3) fresh script re-run (fixed seed, logged) → (10.4) manuscript auto-sync (grep for v{N} residue) → (10.5) supplementary regeneration (consensus log, RoB, GRADE/SoF, PRISMA flow) → (10.6) figure regeneration via /make-figures → (10.7) change summary with delta table → (10.8) PROSPERO amendment (application correction, not criteria change) → (10.9) re-circulation in the Phase 9 thread with the "On re-review" framing → (10.10) anti-patterns to avoid (hide-and-submit, "minor revision" reframe, cover-letter-only disclosure) → (10.11) post- submission escalation path → (10.12) post-recovery loop (Phase 9 restart; tighten Phase 6b if a second sprint is needed).

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).

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Failure Modes (prior MA projects, anonymized)

Failure patterns observed across three prior MA projects (anonymized). Each topical reference extends the phase it cross-references above — consult alongside phase procedural docs, not in isolation.

Domain	Phase span	Load-on-demand reference
Data integrity (2x2 arm-swap, KM audit, methodology mismatch, PRISMA 5-way drift, single-source k)	Phase 3 → 6	references/data_integrity_checklist.md (DI-1~DI-9)
Review orchestration (2nd-reviewer blocking, dual-rating completeness, defensive-tone audit, response-matrix tracking)	Phase 9 circulation (extends phase9_circulation.md)	references/review_orchestration.md (RO-1~RO-5)
Submission package drift (multi-journal folder hygiene, DO_NOT_EDIT_HERE gate, build artifact vs master)	Phase 8 → submission	references/submission_package_drift.md
Post-submission release ops (Zenodo DOI timing, tag-cleanup gate, reject-retarget versioning)	Submission → Phase 10	references/post_submission_release_ops.md

Automation hooks (invoke at the phase listed)

When	Script	Gate
Phase 3f reconciliation (before Phase 5 write-up)	python3 ${CLAUDE_SKILL_DIR}/scripts/check_exclusion_code_validity.py --protocol 0_Protocol/protocol.md --screening 2_Screening/*.tsv --strict	validates each applied exclusion code against the registered eligibility criteria: CODE_CONTRADICTS_ELIGIBILITY (a code excludes a design the protocol includes — the bulk study-loss defect no arithmetic/inter-rater gate can see), CODE_NOT_REGISTERED (off-protocol code), CODE_RENUMBERED (same code, two meanings). Challenge card: scripts/check_exclusion_code_validity_challenge/.
Phase 4 kickoff (before first extraction row)	python3 ${CLAUDE_SKILL_DIR}/../../scripts/extraction_consensus_log_init.py --output 2_Data/extraction_consensus_log.md	DI-1: creates standalone consensus log so comparative arm-specific rows are never folded into R-script comments.
Phase 3f reconciliation + every revision touching PRISMA numbers	python3 ${CLAUDE_SKILL_DIR}/../../scripts/prisma_5way_consistency.py --ssot prisma.yaml	DI-6: 5-surface drift check (abstract / main text / flow figure / supplement / CSV) against YAML SSOT. Non-zero exit blocks Phase 5 writeup.
Phase 8 pre-submission + every journal retarget	bash ${CLAUDE_SKILL_DIR}/../../scripts/tag_cleanup_gate.sh	DI-8: fails if VERIFY-CSV/TODO/FIXME/XXX survive in 7_Manuscript, supplement, SUBMISSION, etc.
Phase 8 on first build per journal (--record), then before every re-submission (--verify)	python3 ${CLAUDE_SKILL_DIR}/../../scripts/verify_package_integrity.py --record --journal <name> then --verify --journal <name>	SPD: checksum-based drift detection between master manuscript and built SUBMISSION/{journal}/ folder. Journal-editable files (cover letter, response, MANIFEST, DO_NOT_EDIT_HERE.md) are auto-excluded.

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

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Empirical Lessons (peer-review cycles)

Sixteen accumulated SR-MA peer-review / submission lessons (2026-05 and 2026-06) — the drivers behind the Phase 4 extraction-form schema, the Phase 4c QC scripts, and the Phase 8 submission gates. To keep this entry point lean they live load-on-demand in ${CLAUDE_SKILL_DIR}/references/empirical_lessons.md. Load that file when designing the extraction form (before Phase 4) and before submission (Phase 8) — it covers dual-extractor 2x2 integrity, cohort-overlap clustering, small-k subgroup caution, the supplementary 8-file bar, PROSPERO ID format, AI-disclosure presence, recompute-don't-copy sensitivity analyses, outcome harmonization, heterogeneous-RoB κ, survival-specific concerns, supplement blinding / de-scaffolding, self-contained reproducible analysis scripts, sidecar re-sync, methodological

• software citations, wide-table PDF rendering, and submission-portal journal-identity checks.

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DTA-Specific Pitfalls (Always Check)

Pitfall	Problem	Solution
Separate pooling of Se/Sp	Ignores correlation	Use bivariate/HSROC model
Ignoring threshold effect	False heterogeneity	Check Spearman correlation, SROC plot
Standard funnel plot for DTA	Inappropriate	Use Deeks' funnel plot
I-squared only for heterogeneity	Doesn't capture threshold effect	Use prediction region on SROC
Missing GRADE	Common omission in DTA MA	Apply GRADE-DTA. If <4 studies, assess each domain narratively and state the limitation explicitly
Partial verification bias	Inflates sensitivity	QUADAS-3 3.2 (target condition assessed in all participants). QUADAS-3 has no Flow & Timing domain — that was QUADAS-2
Differential verification bias	Distorts both Se and Sp	QUADAS-3 3.3 (target condition assessed the same way in all participants)
Unevaluable results excluded	Biases accuracy estimates	Report intent-to-diagnose analysis

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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

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Skill Interactions

When	Call	Purpose
Need literature search	/search-lit	PubMed/Semantic Scholar search with verified citations
Need statistical code	/analyze-stats	Execute R/Python analysis scripts
Need figures	/make-figures	PRISMA flow, forest plots, SROC, funnel plots
Need reporting check	/check-reporting	PRISMA-DTA / PRISMA 2020 compliance (includes Step 4c registration / amendment timing)
Need manuscript writing	/write-paper	Full IMRAD manuscript generation
Need self-review	/self-review	Pre-submission quality check
Self-audit recovery entrypoint (Phase 10)	/write-paper Step 7.4a	Recovery branch for polish pipelines that surface structural audit failures
/sync-submission SR-MA gate	/sync-submission	Before submission, verify supplementary package matches all 8 files in templates/supplementary_8file_checklist.md (PRISMA, PROSPERO, search strategy, exclusion list, extraction table, per-study x per-domain RoB, subgroup forests, sensitivity / publication bias). AI Disclosure presence check (cross-link /peer-review Phase 2A P8). Cite-list duplicate check via /verify-refs Gate 5 (duplicate PMID/DOI).

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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.