bias-assessor

# Bias Assessor (risk-of-bias, lightweight) Goal: make evidence quality explicit in a way that is quick, consistent, and auditable. ## Inputs - `papers/extraction_table.csv` ## Outputs - Updated `papers/extraction_table.csv` ## Recommended fields Use a simple 3-level scale (all lowercase): `low | unclear | high`. Suggested columns to add (if missing): - `rob_selection` - `rob_measurement` - `rob_confounding` - `rob_reporting` - `rob_overall` - `rob_notes` ## Workflow 1. Read `papers/extraction_table.csv` and identify the set of included studies. 2. If RoB columns are missing, add them (keep names stable once introduced). 3. For each study, fill each RoB domain: - `low`: design/reporting plausibly controls the bias - `unclear`: not enough information to judge - `high`: clear risk (e.g., missing controls, ambiguous measurement, selective reporting) 4. Set `rob_overall` conservatively: - `high` if any domain is `high` - `unclear` if no `high` but at least one `unclear` - `low` only if all domains are `low` 5. Add 1–3 short notes in `rob_notes` that justify the rating. ## Definition of Done - [ ] Every included paper row has all RoB columns filled. - [ ] Values are strictly from `low|unclear|high` (no free-form scale drift). - [ ] Notes are short and specific (what was missing / what was strong). ## Troubleshooting ### Issue: the table has mixed or inconsistent RoB column names **Fix**: - Normalize to the recommended column names and keep a single set across all rows. ### Issue: the paper lacks enough methodological detail **Fix**: - Prefer `unclear` with a concrete note (“no details on X”) rather than guessing.