bias-assessor（偏见评估员）

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)

1. 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

1. 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.