Using a distribution-based approach and systematic review methods to derive minimum clinically important differences
Bibliographic record
Abstract
BACKGROUND: Clinical interpretation of changes measured on a scale is dependent on knowing the minimum clinically important difference (MCID) for that scale: the threshold above which clinicians, patients, and researchers perceive an outcome difference. Until now, approaches to determining MCIDs were based upon individual studies or surveys of experts. However, the comparison of meta-analytic treatment effects to a MCID derived from a distribution of standard deviations (SDs) associated with all trial-specific outcomes in a meta-analysis could improve our clinical understanding of meta-analytic treatment effects. METHODS: We approximated MCIDs using a distribution-based approach that pooled SDs associated with baseline mean or mean change values for two scales (i.e. Mini-Mental State Exam [MMSE] and Alzheimer Disease Assessment Scale - Cognitive Subscale [ADAS-Cog]), as reported in parallel randomized trials (RCTs) that were included in a systematic review of cognitive enhancing medications for dementia (i.e. cholinesterase inhibitors and memantine). We excluded RCTs that did not report baseline or mean change SD values. We derived MCIDs at 0.4 and 0.5 SDs of the pooled SD and compared our derived MCIDs to previously published MCIDs for the MMSE and ADAS-Cog. RESULTS: We showed that MCIDs derived from a distribution-based approach approximated published MCIDs for the MMSE and ADAS-Cog. For the MMSE (51 RCTs, 12,449 patients), we derived a MCID of 1.6 at 0.4 SDs and 2 at 0.5 SDs using baseline SDs and we derived a MCID of 1.4 at 0.4 SDs and 1.8 at 0.5 SDs using mean change SDs. For the ADAS-Cog (37 RCTs, 10,006 patients), we derived a MCID of 4 at 0.4 SDs and 5 at 0.5 SDs using baseline SDs and we derived a MCID of 2.6 at 0.4 SDs and 3.2 at 0.5 SDs using mean change SDs. CONCLUSION: A distribution-based approach using data included in a systematic review approximated known MCIDs. Our approach performed better when we derived MCIDs from baseline as opposed to mean change SDs. This approach could facilitate clinical interpretation of outcome measures reported in RCTs and systematic reviews of interventions. Future research should focus on the generalizability of this method to other clinical scenarios.
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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 | 0.258 | 0.516 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.040 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".