Factors associated with functional impairment in symptomatic knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: Knee osteoarthritis (OA) is a major cause of disability, particularly in the elderly. The factors determining disability remain unclear. The aim of this study was to assess the impact of clinical and psychosocial variables on function in knee OA and to develop models to account for observed variance in self-reported disability. METHODS: The subjects (n = 69) were hospital out-patients. Self-reported disability was measured by the Western Ontario and McMaster Universities (WOMAC) OA index. Pain was measured by the WOMAC and the McGill pain questionnaire. Depression, anxiety, helplessness, self-efficacy, fatigue and quality of life were measured by standard instruments. A detailed knee examination, including pain threshold by dolorimetry, was performed. Radiographs were scored for individual features. RESULTS: Pain severity, obesity and helplessness were the most important determinants of disability: a model including these variables accounted for 59.9% variance in WOMAC disability. Anxiety remained associated with disability in some models. Disability was unrelated to radiographic change. CONCLUSIONS: Function in symptomatic knee OA is determined more by pain and obesity than by structural change, at least as seen on plain X-ray. Our study provides further support for interventions targeting anxiety and helplessness in 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".