com um clique
effect-size-extraction
// Systematically extract effect sizes and conditions from papers for meta-analytic synthesis
// Systematically extract effect sizes and conditions from papers for meta-analytic synthesis
SOTA Performance Baseline Campaign — 5 strategies for systematically collecting, standardizing, and analyzing performance data across methods. Produces standardized comparison tables, progress curves, and headroom analysis.
Assess systematic biases in the evidence body — publication bias, reporting bias, and selective outcome reporting. Budget: 40 studies, 40 effect sizes, 40 web searches.
Track evidence accumulation over time — cumulative meta-analysis protocol design. Budget: 40 studies, 40 effect sizes, 30 web searches.
Design structured data extraction form for systematic meta-analysis data collection
Determine effect size types and calculation methods for meta-analytic synthesis
Build evidence network graph for network meta-analysis — nodes, edges, geometry assessment
| name | effect-size-extraction |
| description | Systematically extract effect sizes and conditions from papers for meta-analytic synthesis |
| execution | tactic |
| used-by | meta-analysis |
Systematically extract quantitative effect sizes, study conditions, and precision metrics from included studies.
Identify the specific sections of each paper containing quantitative results.
Tools: dare-scholar (paper_content, paper_reading), alphaxiv (answer_pdf_queries)
For each study, locate the exact data points needed for effect size calculation.
SOPs: effect-size-planning (determine what to extract)
Plan the calculation method for each study based on available data.
SOPs: effect-size-planning
Record all study-level conditions and moderator variables.
SOPs: data-extraction-form
Annotate each extracted effect size with quality indicators.
SOPs: risk-of-bias-assessment
Per execution of this tactic:
extractions:
- study_id: [identifier]
effect_size_type: [SMD/OR/RR/MD/r]
point_estimate: [value or calculation formula]
precision: [SE/CI/variance]
sample_size: [N_treatment, N_control]
conditions: [moderator variables]
quality: [high/medium/low confidence]
notes: [calculation method, assumptions]