yogsoth-ai Pairwise Synthesis Strategy
Design a pairwise meta-analysis protocol comparing method A vs method B across multiple independent studies.
Purpose
When two methods (interventions, algorithms, approaches) have been compared across multiple studies, synthesize the evidence into a single quantitative estimate of relative performance. This strategy produces the complete protocol — stopping before computation.
Budget
| Resource | Floor | Target |
|---|---|---|
| Studies identified | 20 | 30 |
| Effect sizes extracted | 20 | 30 |
| Web searches | 25 | 40 |
| Quality assessments | 15 | 30 |
Budget gate: cannot exit until 80% of floor met.
State Ledger
<HARD-GATE>
| Metric | Current | Floor | Target | Status |
|--------|---------|-------|--------|--------|
| Studies found | 0 | 20 | 30 | BLOCKED |
| Effect sizes planned | 0 | 20 | 30 | BLOCKED |
| Web searches done | 0 | 25 | 40 | BLOCKED |
| Quality assessed | 0 | 15 | 30 | BLOCKED |
</HARD-GATE>
Available Tactics
| Tactic | When to Use |
|---|---|
| effect-size-extraction | After study identification, extract paired effect sizes |
| quality-assessment-protocol | Assess RoB for each included study |
| evidence-synthesis-planning | After extraction, plan the statistical model |
Available SOPs
| SOP | When to Use |
|---|---|
| pico-formulation | First — frame the comparison question |
| inclusion-criteria-design | After PICO — define what studies qualify |
| effect-size-planning | Determine which effect size metric to use |
| data-extraction-form | Design the extraction template |
| risk-of-bias-assessment | Per-study quality assessment |
| sensitivity-analysis-design | Plan robustness checks |
| publication-bias-assessment | Assess reporting bias |
| meta-analysis-synthesis | Final protocol assembly |
Execution Guidance
- Frame — Run
pico-formulationto structure the comparison - Scope — Run
inclusion-criteria-designto define eligibility - Search — Use dare-scholar and dare-ss to find candidate studies
- Extract — Use
effect-size-extractiontactic for each study - Assess — Use
quality-assessment-protocoltactic for bias risk - Plan — Use
evidence-synthesis-planningtactic for model selection - Synthesize — Run
meta-analysis-synthesisto produce protocol
Iterate steps 3-5 until budget floor is met. Check state ledger before each iteration.
Output Format
protocol:
question: [PICO-structured question]
inclusion_criteria: [eligibility rules]
studies_included: [list with metadata]
effect_size_type: [SMD/OR/RR/MD]
model: [fixed-effect/random-effects]
heterogeneity_plan: [I2, tau2, subgroup, meta-regression]
sensitivity_plan: [leave-one-out, influence diagnostics]
bias_assessment_plan: [funnel plot, Egger's, trim-and-fill]
reporting: PRISMA-2020
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| effect-size-extraction | Systematically extract effect sizes and conditions from papers for meta-analytic synthesis |
| evidence-synthesis-planning | Plan the statistical synthesis approach — model selection, heterogeneity strategy, and reporting |
| quality-assessment-protocol | Methodological quality and bias risk assessment of included studies using validated tools |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| data-extraction-form | Design structured data extraction form for systematic meta-analysis data collection |
| effect-size-planning | Determine effect size types and calculation methods for meta-analytic synthesis |
| inclusion-criteria-design | Define inclusion/exclusion criteria for systematic study selection in meta-analysis |
| meta-analysis-synthesis | Produce final meta-analysis protocol document assembling all planning outputs into PRISMA-compliant protocol |
| pico-formulation | Construct PICO/PECO framework for the meta-analysis research question |
| publication-bias-assessment | Plan funnel plots, Egger's test, trim-and-fill, p-curve, and selection model analyses for publication bias |
| risk-of-bias-assessment | Assess methodological bias using RoB2, PROBAST, or QUADAS-2 validated tools |
| sensitivity-analysis-design | Design leave-one-out, influence diagnostics, subgroup analyses, and robustness checks |