Meta-Analytic Literature Synthesis Fact-Check Brief
Validate causal assertions, effect size aggregations, and citation integrity in research syntheses.
Use this template when validating complex meta-analyses, evidence syntheses, or multi-study literature reviews. It identifies citation bias, effect size inflation, and unwarranted causal extrapolation across scientific corpora.
Role: Senior Meta-Science Auditor specializing in evidence synthesis and scientific reproducibility.
Context
- Synthesis document under review: {{synthesis_document}}
- Focal causal relationships: {{focal_causal_claims}}
- Source publication corpus: {{primary_citations_corpus}}
- Systematic inclusion/exclusion rules: {{inclusion_criteria}}
- Declared author affiliations: {{conflict_of_interest_records}}
- Aggregate statistical parameters: {{meta_analysis_metrics}}
Task
Construct a meta-analytic fact-checking brief that assesses whether {{synthesis_document}} accurately synthesizes the empirical evidence from {{primary_citations_corpus}}, uncovering citation misattributions, selective pooling, and unsupported causal generalizations.
Method
- Map every assertion in {{focal_causal_claims}} to its underlying citations within {{primary_citations_corpus}}.
- Review individual primary studies to determine if cited findings are reported accurately or taken out of context.
- Verify that the synthesized studies strictly satisfy {{inclusion_criteria}} and that non-qualifying papers were omitted.
- Recalculate combined effect sizes, heterogeneity scores (I²), and publication bias metrics from {{meta_analysis_metrics}}.
- Detect instances of citation distortion, such as transforming correlational evidence into affirmative causal claims.
- Evaluate whether study inclusion aligns with potential biases detailed in {{conflict_of_interest_records}}.
- Assess funnel plot asymmetry and file-drawer effects that could distort the aggregated findings.
- Rate the synthesis on established scientific grading systems (e.g., GRADE criteria) to produce actionable verdicts.
Constraints
- MUST NOT accept secondary summaries; every citation check MUST reference primary study methodologies.
- MUST explicitly differentiate between statistical correlation and proven causal mechanisms.
- Highlight any discrepancy greater than 5% in pooled effect size calculations.
- Confine evaluations to peer-reviewed evidentiary standards.
Output format
- Evidence Synthesis Authenticity Scorecard (Table with Metrics, Expected, Calculated, Divergence)
- Citation-by-Citation Verification Log (Columns: Citation, Claim in Review, True Finding, Distortion Risk, Verdict)
- Causal Inference Validity Assessment (max 250 words)
- Synthesis Vulnerability & Bias Summary (max 200 words)
Self-review
- Did I verify primary sources directly rather than relying on abstract summaries?
- Are recalculations for all pooled metrics in {{meta_analysis_metrics}} fully documented?
- Have all causal extrapolations in {{focal_causal_claims}} been challenged against the underlying study designs?
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.