The Influence of Pathology, Pain, Balance, and Self-efficacy on Function in Women With Osteoarthritis of the Knee
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The determinants of function in people with osteoarthritis include both psychosocial and physiological variables. Studies that simultaneously integrate both domains are limited. The aim of this study was to determine the following in a sample of women with osteoarthritis (OA) of the knee: (1) the relationships among pathology (grade of OA of the knee), pain level, balance, and self-efficacy and (2) the relative effects of pathology, pain level, balance, and self-efficacy on function. SUBJECTS: Fifty community-dwelling women, 50 to 84 years of age (mean = 69.2, SD = 8.8), with symptoms of OA of the knee participated. METHODS: Radiographs, standardized questionnaires (the Western Ontario and McMaster Universities Osteoarthritis Index, the Arthritis Self-Efficacy Scale, the Functional Reach Test, and timed performance tests were used to quantify the variables. Bivariate analyses and stepwise multiple regression modeling with analysis of variance calculations for beta weight testing were used in data analysis. RESULTS: In regression analysis, functional self-efficacy and balance accounted for 42% of the variance in physical performance of function, whereas functional self-efficacy and pain accounted for 74% of the variance in self-report of functional difficulty. DISCUSSION AND CONCLUSION: Functional self-efficacy is an important factor affecting the functional performance outcome for people with OA of the knee. Suggestions are given to address self-efficacy in health care management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".