Marginal evidence gain research stopping
Parent: Concept knowledge architecture strategies · Published reference · snapshot 2026-10-22
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Source-anchored research for Marginal evidence gain research stopping. Run status: BUDGET_EXHAUSTED.
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How it works
- During screening, true recall remains unknown until all candidate documents have been assessed because the total relevant-document count is unknown. (low confidence) [source]
Comparisons and alternatives
- SAFE explicitly presents its numerical percentages and record counts as adaptable examples rather than universally applicable rules. (low confidence) [source]
Facts and statements
- Callaghan and Müller-Hansen formulate stopping as rejecting a null hypothesis that achieved recall is below a chosen target. (low confidence) [source]
- Their random-sampling criterion uses a hypergeometric model for relevant records drawn without replacement from the finite unscreened pool. (low confidence) [source]
- SAFE combines random training-set screening, active learning, a different screening model, and a final quality-check phase. (low confidence) [source]
- Callaghan and Müller-Hansen report inconsistent achieved recall and work reductions across datasets for previously proposed stopping methods. (low confidence) [source]
- The 2024 stopping-criteria commentary warns that observed reliability can depend on datasets, ranking algorithms, hyperparameters, and implementation details. (low confidence) [source]
- SAFE warns that using expert-selected key papers as training seeds can bias the screening model. (low confidence) [source]
- The SAFE paper states that it does not present a direct comparison with existing stopping heuristics. (low confidence) [source]
- Guest, Namey, and Chen calculate new information over consecutive observation runs relative to unique themes in a specified initial base. (low confidence) [source]
- Guest, Namey, and Chen explicitly state that their new-information thresholds lack a probability-theory guarantee of saturation. (low confidence) [source]
- Their saturation method was tested on single-tier codebooks; hierarchical applications require choosing which coding level contributes to the calculation. (low confidence) [source]
- Hennink, Kaiser, and Marconi distinguish code saturation, where new issues stop appearing, from meaning saturation, where new dimensions of issues stop appearing. (low confidence) [source]
- In the Hennink, Kaiser, and Marconi study, some conceptual codes reached meaning saturation between interviews 16 and 24, while the conceptual code responsibility for health never reached meaning saturation. (low confidence) [source]
- In that study, higher code prevalence did not indicate earlier meaning saturation. (low confidence) [source]
- Repke and colleagues evaluated stopping methods using 223 simulated screening runs over 81 real-world datasets. (low confidence) [source]
- In those experiments, CMH never stopped before the specified recall target, but often screened substantially beyond the optimal stopping point. (low confidence) [source]
- Repke and colleagues evaluate both missed recall targets and additional screening after the target's theoretically optimal stopping point. (low confidence) [source]
- Repke and colleagues disclose that evaluation co-author Finn Müller-Hansen also co-authored CMH. (low confidence) [source]
Children
- Decision value of information (frontier)
- Living-review update triggers (frontier)
Frontier under this node: Decision value of information, Living-review update triggers