quality-assessment-protocol
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/quality-assessment-protocolAssesses methodological quality and bias risk in studies using validated tools
- Evaluates study validity for inclusion in meta-analyses
- Uses RoB 2.0, ROBINS-I, QUADAS-2, PROBAST, and Newcastle-Ottawa tools
- Matches assessment tool to study design and evaluates each domain systematically
- Generates documented risk-of-bias judgments and visual summaries
SKILL.md
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---
name: quality-assessment-protocol
description: Methodological quality and bias risk assessment of included studies using validated tools
execution: tactic
used-by: meta-analysis
---
# Quality Assessment Protocol Tactic
Systematically assess methodological quality and risk of bias for each included study using domain-appropriate validated tools.
## Stages
### Stage 1: Tool Selection
Select the appropriate quality assessment tool based on study design.
| Study Design | Tool | Domains |
|--------------|------|---------|
| RCT | RoB 2.0 | Randomization, deviations, missing data, measurement, selection |
| Non-randomized interventions | ROBINS-I | Confounding, selection, classification, deviations, missing, measurement, reporting |
| Diagnostic accuracy | QUADAS-2 | Patient selection, index test, reference standard, flow/timing |
| Prediction models | PROBAST | Participants, predictors, outcome, analysis |
| Observational | Newcastle-Ottawa | Selection, comparability, outcome/exposure |
**Decision**: Match tool to study design. Use one tool consistently within a meta-analysis.
### Stage 2: Per-Study Assessment
Apply the selected tool to each included study.
- Assess each domain independently
- Use signaling questions to guide domain judgments
- Assign domain-level judgment (Low/Some Concerns/High risk)
- Document supporting rationale for each judgment
- Resolve disagreements with explicit criteria
**SOPs**: risk-of-bias-assessment
### Stage 3: Summary and Visualization Planning
Plan the summary presentation of quality assessments.
- Traffic light plot (per-study, per-domain)
- Summary bar chart (proportion at each risk level per domain)
- Overall risk-of-bias judgment per study
- Sensitivity analysis groupings (low-risk only vs all)
- GRADE certainty assessment contribution
**SOPs**: sensitivity-analysis-design
## Minimum Yield
Per execution of this tactic:
- At least 5 studies assessed
- All domains of the selected tool evaluated per study
- Supporting rationale documented for each judgment
- Summary visualization plan produced
- Sensitivity groupings defined
## Output Format
```yaml
quality_assessment:
tool_used: [RoB2/ROBINS-I/QUADAS-2/PROBAST/NOS]
assessments:
- study_id: [identifier]
domains:
- domain: [name]
judgment: [Low/Some Concerns/High]
rationale: [supporting text]
overall: [Low/Some Concerns/High]
summary:
low_risk_count: [N]
some_concerns_count: [N]
high_risk_count: [N]
problematic_domains: [most common high-risk domains]
sensitivity_groups:
low_risk_only: [study list]
excluding_high_risk: [study list]
```
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