Development of Multinational Definitions of Minimal Clinically Important Improvement and Patient Acceptable Symptomatic State in Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The ability to interpret scores from patient-reported outcome measures at the individual patient level depends on the availability of valid, clinically meaningful benchmarks of response and state attainment. The goal was to develop multinational estimates for minimal clinically important improvement (MCII) and patient acceptable symptomatic state (PASS). METHODS: A multinational sample of patients with osteoarthritis (OA) was evaluated before and 4 weeks after treatment with nonsteroidal antiinflammatory drugs. Patients completed either the Western Ontario and McMaster Osteoarthritis Index (WOMAC) numerical rating scale 3.1 (hip and knee OA) or the Australian/Canadian Index (AUSCAN) numerical rating scale 3.1 (hand OA) before and after treatment. Patients rated the clinical importance of their response to treatment and their satisfaction with the health state achieved, from which multinational MCII and PASS estimates were calculated for both the WOMAC and AUSCAN indices. RESULTS: A total of 609 patients from 7 countries participated in the study. MCII and PASS estimates varied slightly by instrument and subscale. Absolute (percentage) change for MCII ranged 6-9 (10% to 17%) for WOMAC and 4-9 (8% to 15%) for AUSCAN. PASS estimates ranged 39-48 for WOMAC and 38-45 for AUSCAN. Some between-country variation was observed in MCII and PASS. CONCLUSION: Preliminary multinational estimates for MCII and PASS have been developed for several countries. Further research is required to evaluate the robustness, temporal consistency, and age- and sex-dependency of the preliminary estimates as well as their generalizability to other countries, languages, cultures, regions, and other condition-specific outcome measures.
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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 | 0.045 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".