菜单
Track evidence accumulation over time — cumulative meta-analysis protocol design. Budget: 40 studies, 40 effect sizes, 30 web searches.
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基于 SOC 职业分类
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.
Design structured data extraction form for systematic meta-analysis data collection
Systematically extract effect sizes and conditions from papers for meta-analytic synthesis
Determine effect size types and calculation methods for meta-analytic synthesis
Build evidence network graph for network meta-analysis — nodes, edges, geometry assessment
| name | cumulative-tracking |
| description | Track evidence accumulation over time — cumulative meta-analysis protocol design. Budget: 40 studies, 40 effect sizes, 30 web searches. |
Design a cumulative meta-analysis protocol tracking how evidence evolves over time as new studies are published.
Cumulative meta-analysis adds studies one-by-one in chronological order, showing when the evidence became conclusive, whether early studies were misleading, and how the pooled estimate stabilized. This strategy produces the protocol for temporal evidence tracking.
| Resource | Floor | Target |
|---|---|---|
| Studies identified | 28 | 40 |
| Effect sizes extracted | 28 | 40 |
| Web searches | 20 | 30 |
| Temporal coverage (years) | 5 | 10+ |
| Quality assessments | 20 | 40 |
Budget gate: cannot exit until 80% of floor met.
<HARD-GATE>
| Metric | Current | Floor | Target | Status |
|--------|---------|-------|--------|--------|
| Studies found | 0 | 28 | 40 | BLOCKED |
| Effect sizes planned | 0 | 28 | 40 | BLOCKED |
| Web searches done | 0 | 20 | 30 | BLOCKED |
| Year range covered | 0 | 5 | 10+ | BLOCKED |
| Quality assessed | 0 | 20 | 40 | BLOCKED |
</HARD-GATE>
| Tactic | When to Use |
|---|---|
| effect-size-extraction | Extract effect sizes with publication dates |
| quality-assessment-protocol | Assess quality evolution over time |
| evidence-synthesis-planning | Plan cumulative pooling approach |
| SOP | When to Use |
|---|---|
| pico-formulation | Frame the temporal evidence question |
| inclusion-criteria-design | Define eligibility with temporal scope |
| effect-size-planning | Standardize effect sizes for temporal pooling |
| data-extraction-form | Template with mandatory date fields |
| risk-of-bias-assessment | Per-study assessment (track quality trends) |
| heterogeneity-source-analysis | Time-varying heterogeneity |
| sensitivity-analysis-design | First-study effect, vintage analysis |
| publication-bias-assessment | Time-lag bias assessment |
| meta-analysis-synthesis | Final cumulative protocol assembly |
pico-formulation with temporal dimension explicitinclusion-criteria-design with date range requirementseffect-size-extraction tactic with date metadataquality-assessment-protocol noting temporal trendsevidence-synthesis-planning for cumulative modelmeta-analysis-synthesis for final protocolEnsure no temporal gaps. Flag periods with no publications.
protocol:
question: [PICO with temporal dimension]
temporal_scope: [start_year - end_year]
inclusion_criteria: [eligibility with date requirements]
studies_included:
- [study, year, effect_size, cumulative_n]
chronological_order: [sorted study list]
effect_size_type: [consistent metric across time]
model: [random-effects with cumulative pooling]
temporal_analyses:
- cumulative_forest_plot
- first_study_effect_test
- evidence_stabilization_point
- vintage_regression
time_lag_bias: [assessment plan]
quality_trend: [RoB evolution over time]
reporting: PRISMA-2020 + temporal extension