Effect of Physical Activity on Functional Performance and Knee Pain in Patients With Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: A previous analysis of the Osteoarthritis Initiative study reported a dose-response relationship between physical activity and improved physical function in adults with knee osteoarthritis, using conventional statistical methods. These methods are subject to bias when confounders are affected by prior exposure. METHODS: We used baseline and 1-, 2-, and 3-year follow-up data from the Osteoarthritis Initiative study of 2545 US adults with knee osteoarthritis recruited between 2004 and 2006 from 4 clinical sites. Physical activity was measured using the Physical Activity Scale for the Elderly, and outcomes were functional performance measured by the timed 20-meter walk test and self-reported knee pain measured by the Western Ontario and McMaster Universities Osteoarthritis Index. We estimated the effect of physical activity on each outcome using inverse probability-weighted (IPW) estimators of marginal structural models. For each outcome, we fitted 2 separate IPW models adjusting for concurrent or lagged confounders. RESULTS: The mean differences in walking speed for the second, third, and fourth quartiles of physical activity relative to the first were 0.48 (95% confidence interval = -0.12 to 1.08), 0.45 (-0.23 to 1.13), and 0.46 (-0.29 to 1.22) meters/min based on the IPW model adjusting for concurrent confounders. When adjusting for lagged confounders, the results were 1.35 (0.64 to 2.07), 1.33 (0.54 to 2.14), and 1.26 (0.40 to 2.12). Both IPW models indicated that physical activity did not affect knee pain. CONCLUSIONS: Physical activity has no effect on knee pain and may have either a very small effect or no effect on functional performance in adults with knee osteoarthritis.
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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 | 0.004 | 0.017 |
| 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 | 0.000 |
| 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".