Factors Associated with Kinesiophobia in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Abstract Purpose This study aims to determine factors affecting kinesiophobia in patients with knee osteoarthritis (OA). Materials and Methods The fear of movement was measured using the Tampa Scale of Kinesiophobia in 60 patients with knee OA. Pain intensity was assessed with the Visual Analog Scale, quality of life with the Nottingham Health Profile (NHP), disability with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), anxiety and depression with the Hospital Anxiety and Depression Scale (HADS), balance with the Berg Balance Scale, mobility with the Timed Up and Go Test, and the physical activity status was measured with the International Physical Activity Questionnaire. Results Physical mobility and emotional reactions subscales of NHP, all WOMAC subscales and the HADS depression subscale were significantly related to kinesiophobia. Muscle strength, ROM, level of physical activity, balance, mobility and anxiety were not significantly related to kinesiophobia. Quality of life and disability explained 34.4% of the variation in the Tampa Scale of Kinesiophobia. Conclusions Kinesiophobia was associated with quality of life, disability and depression. It may be useful for clinicians to pay attention to the evaluation of psychosocial characteristics instead of physical performance parameters in order to increase treatment success in OA patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".