Impact of osteoarthritis on quality of life in a Hong Kong Chinese population
Bibliographic record
Abstract
Objective. To measure the impact of osteoarthritis (OA) on quality of life in the Hong Kong Chinese population. Methods. This was a cross sectional, retrospective, non-random, cohort design stratifying disease severity and presence or absence of joint prostheses. Patients with OA (n = 574; 136 men and 438 women) were recruited from rheumatology, family medicine, orthopedics, and geriatric medicine clinics. They were divided into 2 equal groups based upon disease severity (either American College of Rheumatology functional classes I and II, or III and IV). The 36-item Medical Outcomes Study Short-Form Health Survey (SF-36) and Western Ontario and McMaster Universities (WOMAC) OA Index were used. Results. Patients with severe disease had lower mean scores in all SF-36 domains and higher mean scores in all WOMAC domains, indicating poorer quality of life. Scores in patients who had had arthroplasty were better than those with severe disease only in certain domains: role physical, general health, vitality, and mental health (SF-36); and pain (WOMAC). Women with OA had poorer scores compared to men for bodily pain, general health, and mental health after adjusting for age and disease severity. Low educational attainment was independently associated with poorer scores when disease severity was taken into account. Conclusion: OA has a significant impact on quality of life, only partly ameliorated by arthroplasty, as assessed by the SF-36 and WOMAC in this population.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".