Do Psychosocial Factors Predict Muscle Strength, Pain, or Physical Performance in Patients With Knee Osteoarthritis?
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the relationship of psychosocial factors, namely, pain catastrophizing, kinesiophobia, and maladaptive coping strategies, with muscle strength, pain, and physical performance in patients with knee osteoarthritis (OA)-related symptoms. METHODS: A total of 109 women (64 with knee OA-related symptoms) with a mean age of 65.4 years (49-81 years) were recruited for this study. Psychosocial factors were quantified by the Pain Catastrophizing Scale, Tampa Scale for Kinesiophobia, and Pain Coping Inventory. Clinical features were assessed using isometric and isokinetic knee muscle strength measurements, visual analog scale, Western Ontario and McMaster Universities Osteoarthritis Index, and functional tests. Associations were examined using correlation and regression analysis. RESULTS: In knee OA patients, pain catastrophizing, kinesiophobia, and coping strategy explained a significant proportion of the variability in isometric knee extension and flexion strength (6.3%-9.2%), accounting for more overall variability than some demographic and medical status variables combined. Psychosocial factors were not significant independent predictors of isokinetic strength, knee pain, or physical performance. CONCLUSIONS: In understanding clinical features related to knee OA, such as muscle weakness, pain catastrophizing, kinesiophobia, and coping strategy might offer something additional beyond what might be explained by traditional factors, underscoring the importance of a biopsychosocial approach in knee OA management. Further research on individual patient characteristics that mediate the effects of psychosocial factors is, however, required in order to create opportunities for more targeted, personalized treatment for knee OA.
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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 | 0.004 |
| 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 | 0.001 | 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".