Use of Minimal Important Difference for Patient-Reported Outcome Measures in Plastic Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The minimal important difference (MID) is vital to consider when interpreting the clinical importance of observed changes from surgical interventions assessed by patient-reported outcome measures (PROMs). There is no accepted standard for how to calculate MIDs, and uptake in the plastic surgery literature is unknown, leading to methodologic and interpretation issues. METHODS: Medline and Embase were searched to identify all plastic surgery randomized controlled trials (RCTs) using PROMs as outcomes and MID estimation studies for PROMs used by RCTs. Included studies were assessed for uptake and application of MIDs, and MID estimation methodology and values were categorized. RESULTS: A total of 554 RCTs using PROMs as outcomes were identified. Of these, 419 RCTs had the possibility of incorporating a previously published MID. The uptake rate of MIDs was 11.5% ( n = 48 of 419). The most common ways MIDs were applied were to calculate sample size (37.5%) or to determine whether results were clinically important (35.4%). A total of 99 studies estimating MID values for the most common PROMs in plastic surgery, based on our review, were analyzed. The most common estimation methodologies were receiver operating characteristic curve analysis (49%), change difference (31%), and SD (25%). CONCLUSIONS: This review highlights limited uptake and application of MIDs in plastic surgery. The authors propose 4 major barriers: (1) no repository of published MIDs for PROMs used in plastic surgery exists; (2) available MIDs are not specific to plastic surgery populations; (3) high heterogeneity in MID estimation methodology was present; and (4) there are wide ranges in MID values, with no superior choice identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".