method-extract

Method Extraction from Prior Literature

Skill Value

• Problem it solves: Plans cite papers but often miss the methods details needed to justify analysis choices.
• Use when: The user has PubMed, Paperclip, or literature-search results and needs methods grounding.
• Output: Structured methods extraction table with cohort, outcome, model, covariate, and effect-size clues.
• Ask only if: Only when source set, extraction fields, or target SAP section is unclear.
• Do not do: Do not treat abstracts as full-text evidence when methods are unavailable.
• Interaction: Check repo/config/chat evidence first. Ask one decision-changing question at a time; use the current harness's blocking question UI when available, otherwise present numbered choices and wait.

Reads a PubMed result CSV and produces a structured methods-comparison table for the SAP.

When this skill activates

• After method-pubmed has produced analysis/pubmed/<query>-<date>.csv
• During method-plan SAP drafting when method choice needs justification
• Before method-power when you need literature context for the target difference (published effects are typically inflated by publication bias and small-study effects; method-power should justify the target difference as important and/or realistic per DELTA2, Cook et al., BMJ 2018, PMID 30560792)

Prerequisites

• A PubMed result CSV (from method-pubmed) exists
• For --full-text-only, the rows must have non-empty pmcid (open PMC full text), or Paperclip must be able to lookup the paper

Evidence access

When this run needs literature, read references/evidence-access.md first; never invent a citation, effect size, or guideline item.

• Look up here: the full text of each included paper (Methods, Results, statistical-analysis sections, and Table 1) for methods extraction; the CSV abstract is the source only when no lawful full text exists.

Core workflow

Step 0: Context inputs (scan chat first)

If no <path/to/pubmed-results.csv> was passed as the first argument, scan the most recent ~50 chat turns for __CE_PUBMED_RESULTS__ csv=<path> n=<int> .... If found, use that CSV path as the input.

Also scan for __CE_EVIDENCE_MAP__ path=<artifact> sources=pubmed[,paperclip] .... If present, read the evidence map before extraction and use it to prioritize papers, identify full-text-verified claims, and avoid re-asking literature questions already answered there. The PubMed CSV remains the required tabular input for this skill; the evidence map is context, not a replacement CSV.

Print: [pubmed] using results from <path> (n=<N>). If neither an explicit path nor the signal is present, ask the user to load the method-pubmed skill first or pass a CSV path explicitly.

Step 1: Load and prioritize

Read the input CSV. If --full-text-only, drop rows without pmcid and report how many were dropped. Sort by year (descending), then by design (RCT > prospective cohort > retrospective cohort > case-control > case series; a project ordering for reading priority, not an evidence grade). Cap at --max (default 25).

Step 2: Read the paper, not only the abstract

MCP egress (every mode): send PubMed, Paperclip, and other MCP or web tools only search terms, MeSH terms, PMIDs, DOIs, and titles, never data values, identifiers, row text, or data files; an endpoint approval does not cover third-party services.

Full text is the extraction source whenever it is lawfully available; the CSV abstract alone is the last resort. For each paper, in preference order:

1. PubMed MCP full text. For rows with a pmcid, call pubmed_fetch_fulltext (default server), or get_copyright_status and then get_full_text_article (other connectors), with the PMCID. Read the Methods, Results, and statistical-analysis sections and Table 1. Cite PubMed and include the DOI in the output row.
2. Paperclip MCP full text. lookup the paper by doi, pmid, or pmc, then cat the Methods/Results sections or grep/scan for the field cues in Step 3 (for example Cox|logistic|mixed|propensity|primary outcome). The authenticated paperclip CLI is the fallback for the same reads. Paperclip may cover papers without a PMCID.
3. E-utilities fallback when neither MCP server is connected and the row has a pmcid: efetch.fcgi?db=pmc&id=<pmcid>&rettype=xml&retmode=xml, then extract <sec sec-type="methods">, <sec sec-type="results">, and <sec sec-type="statistics">.
4. Abstract only when no lawful full text exists: use the abstract from the input CSV and say so.

Record what was read in an evidence_level column: full_text when any of paths 1-3 was read, abstract otherwise. Under --full-text-only, drop rows that end at abstract and report them.

Step 3: Extract structured fields per paper

For each paper, populate:

Field	Source heuristic
sample_size	"we enrolled N", "Final analytic cohort comprised N", first table-1 N
primary_outcome	"primary outcome was X", "primary endpoint", first sentence after "Outcomes" subsection
outcome_type	continuous / binary / time-to-event / count / ordinal
analysis_population	ITT / per-protocol / mITT / complete-case / multiple-imputation
statistical_method	"Cox proportional hazards", "logistic regression", "linear mixed model", etc.
adjustment_set	covariates listed in the model
software	R + package name, SAS, Stata, Python+statsmodels/sklearn, etc.
multiplicity_control	Bonferroni / FDR / hierarchical / none
effect_size_reported	OR, HR, RR, mean diff, with 95% CI
design	RCT / prospective cohort / retrospective cohort / case-control / cross-sectional / simulation / methods guidance
bias_flags	stated or obvious: selection, confounding, small n, single site, outcome-reporting; none noted otherwise (not a formal risk-of-bias assessment)
checklist_followed	CONSORT / STROBE / TRIPOD / TRIPOD+AI / PRISMA / none stated
evidence_level	full_text or abstract, from Step 2

Be conservative: if the paper does not state a field, write not reported rather than infer. Fields such as adjustment_set, software, and multiplicity_control are rarely in an abstract; on an abstract row expect not reported rather than a guess.

Step 4: Write the comparison table

Save to analysis/pubmed/<query-slug>-methods.csv with one row per paper, keeping the input's pmid, year, first_author, doi, and pmcid columns (method-meta-analysis needs them). Also write analysis/pubmed/<query-slug>-methods-summary.md:

• Modal method (most common) per outcome type
• Method × year matrix (is the field shifting?)
• Software × method matrix (what to install)
• Effect-size range (min / median / max) for each commonly-reported metric, with a note that published estimates are likely inflated and should not be used unadjusted as the power target
• % of papers stating a checklist (low % is itself informative)
• % of rows at full_text versus abstract, so the SAP states how much of the justification rests on abstracts

Step 5: Emit signal for downstream skills

__CE_METHOD_EXTRACT__ csv=<path> n=<count> modal_method=<name>

Completion check before emitting: every row has pmid or doi, design, and evidence_level; no field is filled by inference (not reported instead); the summary's full_text percentage matches the CSV.

The modal method is reported as context for method-model-choice, which selects the analytic method from the estimand, outcome, and data structure; method frequency is never the selection criterion. The comparison table (PMIDs, DOIs, per-row design and evidence_level) is the citation for that context.

What this skill does NOT do

• Does not download PDFs or use unauthorized mirrors (full text via open PMC or a connected Paperclip service only)
• Does not perform formal risk-of-bias or quality appraisal (it records design and obvious bias flags only)
• Does not pool effect sizes (use the method-meta-analysis skill)
• Does not match the study to a checklist (use the method-checklist skill)

References

@./references/extraction-prompts.md

@./references/method-taxonomy.md