Contribution of Psychosocial and Mechanical Variables to Physical Performance Measures in Knee Osteoarthritis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This cross-sectional study evaluated the relative contributions of psychosocial and mechanical variables to physical performance measures in people with knee osteoarthritis (OA). SUBJECTS: Fifty-four subjects (age, in years: mean=68.3, SD=8.7, range=50-87) with radiographically confirmed knee OA were included in this study. METHODS: Physical performance measures included the Six-Minute Walk Test (SMW), the Timed "Up & Go" Test (TUG), and a stair-climbing task (STR). Responses to psychosocial questionnaires that reflect depression, anxiety, and self-efficacy (a person's confidence in his or her ability to complete a task) were collected. Mechanical variables measured included body mass index and knee strength (force-generating capacity of muscle). Stepwise linear regressions were performed with the SMW, TUG, and STR as separate dependent variables. RESULTS: Functional self-efficacy explained the greatest amount of variance in all performance measures, contributing 45% or more. Knee strength and body weight also explained some variance in performance measures. Anxiety and depression did not explain any variance in performance. DISCUSSION AND CONCLUSION: Physical therapists evaluating the significance of the SMW, TUG, and STR scores in subjects with knee OA should note that a large part of each score reflects self-efficacy, or confidence, for physical tasks, with some contributions from knee strength and body weight.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".