Clinically Important Improvement in Function Is Common in People with or at High Risk of Knee OA: The MOST Study
Bibliographic record
Abstract
OBJECTIVE: To calculate the frequency of clinically important improvement in function over 30 months and identify risk factors in people who have or are at risk of knee osteoarthritis (OA). METHODS: Subjects were from the Multicenter Osteoarthritis (MOST), a longitudinal study of persons with or at high risk of knee OA. We defined minimal clinically important improvement (MCII) with the Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) physical function using 3 different methods. Baseline risk factors tested for improvement included age, sex, educational attainment, presence of radiographic knee OA (ROA), the number of comorbidities, body mass index (BMI), knee pain, walking speed, isokinetic knee extensor strength, depressive symptoms, physical activity, and medication usage. We used logistic regression to evaluate the association of baseline risk factors with MCII. RESULTS: Of the 1801 subjects (mean age 63 yrs, BMI = 31, 63% women), most had mild limitations in baseline function (WOMAC = 19 +/- 11). Regardless of how defined, a substantial percentage of subjects (24%-39%) reached MCII at 30 months. Compared to their counterparts, people with MCII were less likely to have ROA and to use medications, and were more likely to have a lower BMI, less knee pain, a faster walking speed, more knee strength, and fewer depressive symptoms. After adjustment, MCII was 40% to 50% less likely in those with ROA, and 1.9 to 2.0 times more likely in those walking 1.0 meters/second faster than counterparts. CONCLUSION: Clinically important improvement is frequent in people with or at high risk of knee OA. The absence of ROA and a faster walking speed appear to be associated with clinically important improvements.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".