Use when formulating clinical research questions (PICOT framework), evaluating health evidence quality (study design hierarchy, bias assessment, GRADE), prioritizing patient-important outcomes, conducting systematic reviews or meta-analyses, creating evidence summaries for guidelines, assessing regulatory evidence, or when user mentions clinical trials, evidence-based medicine, health research methodology, systematic reviews, research protocols, or study quality assessment.
Applies evidence-based medicine frameworks to structure clinical research questions (PICOT), evaluate study quality and bias, and grade evidence certainty for systematic reviews, trial design, or clinical guideline development. Use when formulating research questions, assessing evidence quality, or synthesizing findings for clinical decision-making.
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resources/evaluators/rubric_domain_research_health_science.jsonresources/methodology.mdresources/template.mdThis skill helps structure clinical and health science research using evidence-based medicine frameworks. It guides you through formulating precise research questions (PICOT), evaluating study quality (hierarchy of evidence, bias assessment, GRADE), prioritizing outcomes (patient-important vs surrogate), and synthesizing evidence for clinical decision-making.
Use this skill when:
Trigger phrases: "clinical trial design", "systematic review", "PICOT question", "evidence quality", "bias assessment", "GRADE", "outcome measures", "research protocol", "evidence synthesis", "study appraisal"
Domain Research: Health Science applies structured frameworks from evidence-based medicine to ensure clinical research is well-formulated, methodologically sound, and clinically meaningful.
Quick example:
Vague question: "Does this drug work for heart disease?"
PICOT-structured question:
Result: Precise, answerable research question that guides study design, literature search, and outcome selection.
Copy this checklist and track your progress:
Health Research Progress:
- [ ] Step 1: Formulate research question (PICOT)
- [ ] Step 2: Assess evidence hierarchy and study design
- [ ] Step 3: Evaluate study quality and bias
- [ ] Step 4: Prioritize and define outcomes
- [ ] Step 5: Synthesize evidence and grade certainty
- [ ] Step 6: Create decision-ready summary
Step 1: Formulate research question (PICOT)
Use PICOT framework to structure answerable clinical question. Define Population (demographics, condition, setting), Intervention (treatment, exposure, diagnostic test), Comparator (alternative treatment, placebo, standard care), Outcome (patient-important endpoints), and Timeframe (follow-up duration). See resources/template.md for structured templates.
Step 2: Assess evidence hierarchy and study design
Determine appropriate study design based on research question type (therapy: RCT; diagnosis: cross-sectional; prognosis: cohort; harm: case-control or cohort). Understand hierarchy of evidence (systematic reviews > RCTs > cohort > case-control > case series). See resources/methodology.md for design selection guidance.
Step 3: Evaluate study quality and bias
Apply risk of bias assessment tools (Cochrane RoB 2 for RCTs, ROBINS-I for observational studies, QUADAS-2 for diagnostic accuracy). Evaluate randomization, blinding, allocation concealment, incomplete outcome data, selective reporting. See resources/methodology.md for detailed criteria.
Step 4: Prioritize and define outcomes
Distinguish patient-important outcomes (mortality, symptoms, quality of life, function) from surrogate endpoints (biomarkers, lab values). Create outcome hierarchy: critical (decision-driving), important (informs decision), not important. Define measurement instruments and minimal clinically important differences (MCID). See resources/template.md for prioritization framework.
Step 5: Synthesize evidence and grade certainty
Apply GRADE (Grading of Recommendations Assessment, Development and Evaluation) to rate certainty of evidence (high, moderate, low, very low). Consider study limitations, inconsistency, indirectness, imprecision, publication bias. Upgrade for large effects, dose-response, or confounders reducing effect. See resources/methodology.md for rating guidance.
Step 6: Create decision-ready summary
Produce evidence profile or summary of findings table linking outcomes to certainty ratings and effect estimates. Include clinical interpretation, applicability assessment, and evidence gaps. Validate using resources/evaluators/rubric_domain_research_health_science.json. Minimum standard: Average score ≥ 3.5.
Pattern 1: Therapy/Intervention Question
Pattern 2: Diagnostic Test Accuracy
Pattern 3: Prognosis/Risk Prediction
Pattern 4: Harm/Safety Assessment
Pattern 5: Systematic Review/Meta-Analysis
Critical requirements:
Use PICOT for all clinical questions: Vague questions lead to unfocused research. Always specify Population, Intervention, Comparator, Outcome, Timeframe explicitly. Avoid "does X work?" without defining for whom, compared to what, and measuring which outcomes.
Match study design to question type: RCTs answer therapy questions (causal inference). Cohort studies answer prognosis. Cross-sectional studies answer diagnosis. Case-control studies answer rare harm or etiology. Don't claim causation from observational data or use case series for treatment effects.
Prioritize patient-important outcomes over surrogates: Surrogate endpoints (biomarkers, lab values) don't always correlate with patient outcomes. Focus on mortality, morbidity, symptoms, function, quality of life. Only use surrogates if validated relationship to patient outcomes exists.
Assess bias systematically, not informally: Use validated tools (Cochrane RoB 2, ROBINS-I, QUADAS-2) not subjective judgment. Bias assessment affects certainty of evidence and clinical recommendations. Common biases: selection bias, performance bias (lack of blinding), detection bias, attrition bias, reporting bias.
Apply GRADE to rate certainty of evidence: Don't conflate study design with certainty. RCTs start as high certainty but can be downgraded (serious limitations, inconsistency, indirectness, imprecision, publication bias). Observational studies start as low but can be upgraded (large effect, dose-response, residual confounding reducing effect).
Distinguish statistical significance from clinical importance: p < 0.05 doesn't mean clinically meaningful. Consider minimal clinically important difference (MCID), absolute risk reduction, number needed to treat (NNT). Small p-value with tiny effect size is statistically significant but clinically irrelevant.
Assess external validity and applicability: Evidence from selected trial populations may not apply to your patient. Consider PICO match (are your patients similar?), setting differences (tertiary center vs community), intervention feasibility, patient values and preferences.
State limitations and certainty explicitly: All evidence has limitations. Specify what's uncertain, where evidence gaps exist, and how this affects confidence in recommendations. Avoid overconfident claims not supported by evidence quality.
Common pitfalls:
Key resources:
PICOT Template:
Evidence Hierarchy (Therapy Questions):
GRADE Certainty Ratings:
Typical workflow time:
When to escalate:
Inputs required:
Outputs produced:
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