Many faces of the minimal clinically important difference (MCID): a literature review and directions for future research
Bibliographic record
Abstract
The minimal clinically important difference (MCID) for an instrument is a much sought after, but elusive figure. In this review we will highlight new findings in this area, including taxonomy of MCID, methods used to ascertain MCID, the perspective taken for evaluating importance, and other sources of variation for MCID values. In the end we believe the MCID will be a context-specific value rather than a fixed number. The review highlights the need to do methodological research in this area, especially concurrent comparisons between approaches, or across different patient groups. There are many faces to the MCID, it is not a simple concept, nor simple to calculate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.281 | 0.474 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".