Mind the methods of determining minimal important differences: three critical issues to consider
Bibliographic record
Abstract
OBJECTIVE: Clinical trialists, meta-analysts and clinical guideline developers are increasingly using minimal important differences (MIDs) to enhance the interpretability of patient-reported outcome measures (PROMs). Here, we elucidate three critical issues of which MID users should be aware. Improved understanding of MID concepts and awareness of common pitfalls in methodology and reporting will better inform the application of MIDs in clinical research and decision-making. METHODS: We conducted a systematic review to inform the development of an inventory of anchor-based MID estimates for PROMs. We searched four electronic databases to identify primary studies empirically calculating an anchor-based MID estimate for any PROM in adolescent or adult populations across all clinical areas. Our findings are based on information from 338 studies reporting 3389 MIDs for 358 PROMs published between 1989 and 2015. RESULTS: We identified three key issues in the MID literature that demand attention. (1) The profusion of terms representing the MID concept adds unnecessary complexity to users' task in identifying relevant MIDs, requiring meticulous inspection of methodology to ensure estimates offered truly reflect the MID. (2) A multitude of diverse methods for MID estimation that will yield different estimates exist, and whether there are superior options remains unresolved. (3) There are serious issues of incomplete presentation and reporting of key aspects of the design, methodology and results of studies providing anchor-based MIDs, which threatens the optimal use of these estimates for interpretation of intervention effects on PROMs. CONCLUSIONS: Although the MID represents a powerful tool for enhancing the interpretability of PROMs, realising its full value will require improved understanding and reporting of its measurement fundamentals.
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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 | 0.749 | 0.923 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.016 | 0.011 |
| Research integrity | 0.012 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 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".