extraction-form

SKILL.md

Extraction Form (systematic review)

Goal: create a consistent, analysis-ready extraction table that is directly grounded in the protocol.

Inputs

Required:

  • papers/screening_log.csv
  • output/PROTOCOL.md

Optional:

  • papers/paper_notes.jsonl (if you already have structured notes)

Outputs

  • papers/extraction_table.csv

Workflow

  1. Determine the included set

    • From papers/screening_log.csv, collect all rows with decision=include.
  2. Build/confirm the schema

    • Use the extraction schema defined in output/PROTOCOL.md.
    • If the protocol does not define fields yet, stop and update output/PROTOCOL.md first.
  3. Populate papers/extraction_table.csv

    • One row per included paper.
    • If papers/paper_notes.jsonl exists, use it as a structured source for values/provenance (but keep the table schema governed by output/PROTOCOL.md).
    • Always include provenance columns:
      • paper_id, title, year, url
    • For each protocol-defined field:
      • fill concrete values (units explicit)
      • use an explicit sentinel for unknowns (recommended: empty cell + notes)
  4. Keep it auditable

    • If a value is inferred (not directly stated), mark it in a notes column.
    • Do not write synthesis; only extraction.
  5. Quick QA

    • Ensure 1:1 coverage: included papers == extraction rows.
    • Spot-check a few rows against the paper text/notes.

Definition of Done

  • papers/extraction_table.csv exists.
  • Every included paper from papers/screening_log.csv has exactly one extraction row.
  • Column meanings match output/PROTOCOL.md (no ad-hoc columns without updating the protocol).

Troubleshooting

Issue: the protocol does not specify extraction fields

Fix:

  • Update output/PROTOCOL.md (extraction schema section) and re-run extraction.

Issue: extraction table mixes narrative text with fields

Fix:

  • Move narrative into a notes column and keep the rest as atomic values (numbers/enums/short strings).
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