git:20260819.bd9e30b to git:20260930.c8ed96c

- 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

- 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.

-

- ---

-

- | **Intervention** (treatment effect) | RoB 2 (RCT) / ROBINS-I (NRSI) | Random-effects (DL/REML) | PRISMA 2020 |

- | **Prognostic** (prediction model) | QUIPS / PROBAST | Random-effects | PRISMA 2020 |

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

- **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**:

- - 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

- - **Registration-ID format gate.** A PROSPERO ID is `CRD42` + 9 digits (14 characters total), e.g. `CRD42024500001`. Validate any ID that appears in the manuscript or registration doc with `grep -oE 'CRD42[0-9]+'` and assert a 14-character length / `^CRD42\d{9}$` — a 15-character ID (a stray digit) is a transcription error a reviewer will check against the live record.

- - **Review-type selection.** Pick the *least-wrong* portal review type for the actual design and state any portal constraint in the protocol. A descriptive single-arm proportion synthesis is not an "Intervention review"; choosing "Intervention review" only to satisfy a portal field contradicts a later GRADE / effect-certainty statement. Whatever certainty language the protocol commits to (GRADE vs "evidence statements only") must match the manuscript verbatim — a guideline-style "we recommend" is not licensed by a descriptive review type.

- - 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

- 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.

- **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:

- decision; silence is a gap. Never let it settle into narrative-only (why: reference file).

- The set algebra, the reconciliation-table template, and the failure pattern it exists for (a

- manuscript ships counts the ID sets do not support, with every downstream artifact echoing the same

- unreconciled prose total) are in the reference file.

- **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 |

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

- **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.

- Procedure and precedent: reference file.

- `{project}/consensus_log.md`, then **lock the dataset**; later changes need a dated justification.

- 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 |

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

- **IMPORTANT**: Always use R for meta-analysis (packages: `meta`, `metafor`, `mada`).

- See `${CLAUDE_SKILL_DIR}/references/r_templates.md` for full code templates.

- | Intervention | `meta::metagen()` / `meta::metabin()` | Pooled OR/RR, I², Egger's test, leave-one-out |

- **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.

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

- > A safety outcome is reported with its arm-level events, and therefore its p-value,

- > direction-reversed relative to what the primary-source Table actually recorded.

- > The extraction CSV is 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.

- 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:

- ```r

- # 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.

- 5. **Sensitivity analyses must be recomputed on the modified data, not copied.**

- - When you add a sensitivity / leave-one-out / erosion / alternative-model analysis, every

- reported effect size (Cohen's dz/f, AUC, OR, HR, β, sens/spec, ICC) MUST be re-derived from

- the modified dataset. If a sensitivity-table effect size is **identical to the primary

- analysis to two decimals across ≥4 values**, the recomputation almost certainly did not run

- and the primary values were transcribed — re-run the script on the modified data.

- - The underlying means/SDs/counts will change even when the effect size looks similar; if the

- effect sizes are byte-identical while the inputs differ, that is the tell. Probability of ≥4

- independent values coinciding to 2 decimals by chance is ≈ (0.01)^4 — essentially zero.

- - The failure it catches: a sensitivity analysis reports a block of effect-size values

- byte-identical to the primary tables while the underlying means/SDs differ — the

- sensitivity analysis was never actually recomputed. Internal consistency cannot see it.

- 6. **A "fixed" / "resolved" audit note requires re-run evidence, not a claim.**

- - When a prior audit note records a number as `fixed`, `resolved`, or `corrected`, that status is

- only valid if it carries the re-run evidence: a timestamp and the relevant stdout / output-file

- line showing the corrected value, or the commit that changed it. A bare "fixed in v10" with no

- re-run artifact does NOT clear the finding — re-run the script and attach the output.

- - The forward pipeline can echo a stale value through every artifact while an audit note claims it

- was fixed (e.g., a major-comparison N still reading the old total after a "fixed" note). The

- outcome-denominator cross-check (`/self-review` Phase 2.5b, the cohort-arithmetic / pool-lock

- assertions) must pass against the *current* outputs before any "fixed" status is accepted.

- **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.

- 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).

- 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:

- The abstract items are the cheapest of these and the most reliably forgotten. PRISMA 2020

- devotes a **separate 12-item instrument** to the abstract — item 2 of the main checklist does

- nothing but defer to it — so a manuscript can satisfy all 42 main-text items and still fail

- most of the twelve. `/check-reporting` carries it as `PRISMA_2020_Abstracts.md`; run it as its

- own pass and report its score separately, because folding twelve items into a 42-item total is

- how they stay invisible.

- 6. **Data availability statement**: name what is being shared (extraction template,

- locked dataset, analysis code, RoB judgments) and where — repository, DOI, or

- supplementary file. "Available from the corresponding author on reasonable request"

- satisfies few journals now and no longer satisfies item 27. If a Zenodo DOI is minted

- post-acceptance, `references/post_submission_release_ops.md` covers propagating it

- back into this statement.

- 7. **Supplementary & analysis-code pre-submission gate** (run before Phase 9 circulation and before portal upload). Presence of the 8-file package (Empirical Lesson 5) is necessary but not sufficient — each item must also be reviewer-ready:

- - **De-scaffold**: strip internal-QC / tool artifacts before bundling — raw `/check-reporting` output ("Assessed by: <tool>", JSON blocks, "READY FOR SUBMISSION" verdicts, action-item lists), search-development planning docs (decision logs, expected-yield estimates, `[Check on execution]` placeholders, version-history dev notes), and stale version stamps. Ship a clean PRISMA 2020 checklist (27-item / 42-subitem table only) and an executed-method search-strategy doc, not the working drafts.

- - **Blind**: supplementary goes to reviewers — remove author names/initials and sibling-project cross-references ("Designed by: <name>", "identical to a sibling review"). Same standard as the blinded manuscript.

- - **Cross-consistency with the manuscript**: every supplementary number must match the main text — PRISMA counts, pool k/N, the Cochrane/CENTRAL search description, RoB counts. A supplement that says "Cochrane — NOT SEARCHED" while Methods report a confirmatory CENTRAL search is a contradiction reviewers catch.

- - **Submitted analysis code must reproduce and be self-contained**: run it from a clean copy of the bundle. It must (a) read the bundled locked dataset (not an out-of-bundle path) and write to the working directory, and (b) regenerate every pool reported in the results table. A hard-coded study-id subset that drifts from the manuscript (e.g., a pool computed over k=7 while the manuscript reports k=9) is a P0 — fix and re-run; never ship stale code or stale figures derived from it.

- - **Run a supplementary-only review pass** — the manuscript self-review/panel does not see the supplement; mirror `/self-review` Phase 2.5c–2.5d (reference + cross-reference QC) over the supplementary files.

- **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.

- Catching the error yourself before a journal reviewer does is the principal trust-building

- move in this phase.

- **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 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.

-

- ---

-

- ## 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.

-

- ---

-

- | Ignoring threshold effect | False heterogeneity | Check Spearman correlation, SROC plot |

- - Bivariate/HSROC model may not converge -- consider univariate random-effects as fallback

- - Publication bias tests are underpowered -- state this limitation

- - Wide prediction regions expected -- emphasize uncertainty in conclusions

-

- ---

-

- ## 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). |

-

- ---

-

- ## 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.