Determinants of pain and functioning in hip osteoarthritis – a two-year prospective study
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of pain and disability in hip osteoarthritis. DESIGN: A prospective analysis of determinants of pain and functioning in hip osteoarthritis. STUDY SETTING: Rehabilitation clinic in a central hospital. PATIENTS: A total of 118 men and women aged 55-80 years who had radiologically diagnosed hip osteoarthritis and associated clinical symptoms and participated in a randomized controlled trial. MAIN MEASURES: The self-reported disease-specific pain and physical function were assessed using the pain and functioning subscales of the Western Ontario and McMaster Universities (WOMAC) Osteoarthritis index. The self-reported generic physical and mental functioning were assessed by using the Finnish-validated SF-36-item Health Survey RAND-36 subscales for function and physical and mental component summary scores. Outcome measures were recorded at 0, 3, 6, 12, 18 and 24 months. RESULTS: Multivariate linear mixed model analyses revealed that lower disease-specific pain score and better functioning (WOMAC) were predicted by higher educational level (9.61 (3.15 to 16.07); 9.07 (2.05 to 16.09)), supervised exercise training (-10.13 (-17.87 to -2.39); -11.58 (-19.40 to -3.77)), habitual conditioning physical activity (-0.48 (-0.96 to -0.01); -0.39 (-0.84 to 0.05)), absence of comorbidities (-6.30 (-12.35 to -0.24); -7.87 (-14.45 to -1.30)) and absence of additional knee osteoarthritis (-7.62 (-13.87 to -1.36); -8.02 (-14.81 to -1.23)), respectively. The same factors, except for the comorbidities, also predicted general physical functioning score (RAND-36). CONCLUSIONS: Higher education, absence of knee osteoarthritis and comorbidities, supervised exercise training and habitual conditioning physical activity predicted a lower presence of pain and better functional status in patients with hip osteoarthritis.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".