Minimal important changes in standard deviation units are highly variable and no universally applicable value can be determined
Bibliographic record
Abstract
OBJECTIVES: This study aims to describe the distribution of anchor-based minimal important change (MIC) estimates in standard deviation (SD) units and examine if the robustness of such estimates depends on the specific SD used or on the methodological credibility of the anchor-based estimates. DESIGN AND SETTING: We included all anchor-based MIC estimates from studies published in MEDLINE and relevant literature databases upto October 2018. Each MIC was converted to SD units using baseline, endpoint, and change from baseline SDs. We performed a descriptive analysis of MICs in SD units and checked how the distribution would change if MICs with low methodological credibility were excluded from the analysis. RESULTS: We included 1,009 MIC estimates from 182 studies. The medians and interquartile ranges of MICs in SD units were 0.43 (0.25 to 0.69), 0.42 (0.22 to 0.70), and 0.51 (0.28 to 0.78) for baseline, endpoint, and change SD units, respectively. Some MICs were extremely large or small. The distribution did not change significantly after excluding MICs estimated by less credible methods. CONCLUSIONS: The size of the universally applicable MIC in SD units could not be determined. Anchor-based MICs in SD units were widely distributed, with more than half in the range of 0.2 to 0.8.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".