Minimum clinically important improvement and patient acceptable symptom state in pain and function in rheumatoid arthritis, ankylosing spondylitis, chronic back pain, hand osteoarthritis, and hip and knee osteoarthritis: Results from a prospective multinational study
Bibliographic record
Abstract
OBJECTIVE: To estimate the minimum clinically important improvement (MCII) and patient acceptable symptom state (PASS) values for 4 generic outcomes in 5 rheumatic diseases and 7 countries. METHODS: We conducted a multinational (Australia, France, Italy, Lebanon, Morocco, Spain, and The Netherlands) 4-week cohort study involving 1,532 patients who were prescribed nonsteroidal antiinflammatory drugs for ankylosing spondylitis, chronic back pain, hand osteoarthritis, hip and/or knee osteoarthritis, or rheumatoid arthritis. The MCII and PASS values were estimated with the 75th percentile approach for 4 generic outcomes: pain, patient global assessment, functional disability, and physician global assessment, all normalized to a 0-100 score. RESULTS: For the whole sample, the estimated MCII values for absolute change at 4 weeks were -17 (95% confidence interval [95% CI] -18, -15) for pain; -15 (95% CI -16, -14) for patient global assessment; -12 (95% CI -13, -11) for functional disability assessment; and -14 (95% CI -15, -14) for physician global assessment. For the whole sample, the estimated PASS values were 42 (95% CI 40, 44) for pain; 43 (95% CI 41, 45) for patient global assessment; 43 (95% CI 41, 44) for functional disability assessment; and 39 (95% CI 37, 40) for physician global assessment. Estimates were consistent across diseases and countries (for subgroups ≥20 patients). CONCLUSION: This work allows for promoting the use of values of MCII (15 of 100 for absolute improvement, 20% for relative improvement) and PASS (40 of 100) in reporting the results of trials of any of the 5 involved rheumatic diseases with pain, patient global assessment, physical function, or physician global assessment used as outcome criteria.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".