The longitudinal course of physical function in people with symptomatic knee osteoarthritis: Data from the MOST study and the OAI
Bibliographic record
Abstract
Objective Pain and functional decline are hallmarks of knee osteoarthritis (OA). Nevertheless, longitudinal studies unexpectedly reveal stable or improved physical function. The aim of this study was to impute missing and pre–total knee replacement (TKR) values to describe physical function over time among people with symptomatic knee OA. Methods We included participants from the Multicenter Osteoarthritis Study (MOST) and the Osteoarthritis Initiative (OAI) with incident symptomatic knee OA, observed during the first 30 months in MOST and 36 months in OAI. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) physical function (WOMAC-PF), the 5-times sit-to-stand test, and the 20-meter-walk test were assessed at 4 and 5 years in MOST and at 6 years in OAI. We used a multiple imputation method for missing visits, and estimated pre-TKR values close to the time of TKR, using a fitted local regression smoothing curve. In mixed-effect models, we investigated the physical function change over time, using data before and after imputation and calculation of pre-TKR values. Results In MOST, 225 (8%) had incident knee OA, with corresponding 577 (12.7%) in OAI. After adjusting for pre-TKR values and imputing missing values, we found that WOMAC-PF values remained stable or slightly declined over time, and the 20-meter-walk test results changed from stable in nonimputed analyses to worsening using imputed data. Conclusion Data from MOST and OAI showed stable to worsening physical function over time in people with incident symptomatic knee OA after imputing missing values and adjusting pre-TKR values.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".