The Minimally Important Difference in Clinical Practice for Patient-centered Outcomes Including Health Assessment Questionnaire, Fatigue, Pain, Sleep, Global Visual Analog Scale, and SF-36 in Scleroderma
Bibliographic record
Abstract
OBJECTIVE: We studied a large clinical practice and multicenter database to estimate the minimally important difference (MID) in systemic sclerosis (SSc) using global rating of change anchors for the Health Assessment Questionnaire-Disability Index (HAQ-DI) and visual analog scale (VAS) in pain, fatigue, sleep, global status, and the Medical Outcomes Study Short-Form 36 (SF-36) in clinical practice. METHODS: Longitudinal data were collected from a scleroderma clinic on patients with scleroderma (n = 109) who had completed the HAQ-DI and pain/fatigue/sleep/global status VAS (0 to 100 mm) questionnaires at 2 consecutive visits, and rated their change in overall status since the last visit as much better, better, same, worse, or much worse. Data were extracted from the Canadian Scleroderma Research Group (CSRG) database (n = 341) for 2 consecutive annual visits where the patients had completed HAQ-DI and SF-36, and the SF-36 "change in health" item. RESULTS: For the single site, the mean baseline HAQ-DI was 0.895 and 0.911 at followup, with a mean change of 0.016. The MID estimates for improvement and worsening respectively were -0.0125 (0.125, 75th percentile)/0.042 (0.217, 75th percentile) for HAQ-DI, -8.00/3.61 for pain, -10.00/3.79 (25.32) for fatigue, -18.50/5.92 for sleep, and -6.70/4.05 for global VAS. In the CSRG, baseline scores were 0.787 for HAQ-DI, 37.20 for the Physical Component Summary (PCS) of SF-36, and 48.57 for the Mental Component Summary (MCS). The MID estimates for improvement and worsening were -0.037 (0.250, 75th percentile)/0.140 (0.375, 75th percentile) for HAQ-DI, 2.18/-1.74 for PCS, and 1.33/-2.61 for MCS. CONCLUSION: This study provides MID estimates in SSc from 2 large databases for commonly used patient-reported outcomes in a clinical practice setting, which could differ from MID in trials.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".