The Minimally Important Difference for Patient Reported Outcomes in Systemic Lupus Erythematosus Including the HAQ-DI, Pain, Fatigue, and SF-36
Bibliographic record
Abstract
OBJECTIVE: We studied patients with systemic lupus erythematosus (SLE) in 1 clinical practice, and patients enrolled in the 1000 Canadian Faces of Lupus database, to determine the minimally important difference (MID) for pain, fatigue, sleep, Health Assessment Questionnaire-Disability Index (HAQ-DI), and Medical Outcomes Study Short Form-36 (SF-36) Physical Component Score (PCS) and SF-36 Mental Component Score (MCS) using a patient-reported overall health status anchor. METHODS: Patients with SLE who had 2 consecutive clinic visits and completed a HAQ-DI and a pain, fatigue, and sleep visual analog scale (VAS) (0-100), and an overall health status question: "How would you describe your overall status since your last visit?": much better, better, the same, worse, or much worse were included. Those who self-rated as better or worse were considered the "minimally changed" subgroups. Patients with 2 consecutive annual visits in the 1000 Canadian Faces of Lupus database who completed the SF-36 and health transition question were eligible. RESULTS: There were 202 patients in London, Ontario (94% women, mean age 50 yrs, mean disease duration 10 yrs). MID for better and worse on a VAS (0-100) were: pain (-15.8, 8.5), fatigue (-13.9, 9.1), and sleep problems (-8.6, 7.6). The MID for HAQ-DI (scale 0 to 3) was -0.08 (better) and 0.14 (worse). The MID for SF-36 was 2.1 (better) and -2.2 (worse) for the PCS and 2.4 (better) and -1.2 (worse) in the MCS. CONCLUSION: The MID in patients with SLE may be different bidirectionally depending on the measured outcome. The mean change observed for those reporting better than worse outcome in pain and fatigue was greater for better versus worst, in contrast to the HAQ, where the mean change was greater for worsening.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".