Effect of rofecoxib therapy on measures of health-related quality of life in patients with osteoarthritis.
Bibliographic record
Abstract
BACKGROUND: Bodily pain and physical disability can negatively impact health-related quality of life (HRQL) in patients with osteoarthritis (OA). OBJECTIVE: To assess the effects of treatment with a new agent, rofecoxib, on HRQL in patients with OA. STUDY DESIGN: Randomized, double-blind, 6-week clinical trial comparing treatment with rofecoxib, 5 to 50 mg, with placebo in 672 patients with OA of the hip or knee. MAIN OUTCOME MEASURE: Patient HRQL was assessed at baseline and at the end of treatment using the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). RESULTS: At 6 weeks, mean change from baseline in all SF-36 mental and physical health domain scores demonstrated significant improvement with rofecoxib use (P < .05 for all doses for all SF-36 domains), with evidence of a dose-response relation. Improvements in mental and physical HRQL domains with rofecoxib treatment were significantly greater than those with placebo treatment (P < .05 for each dose of rofecoxib vs placebo for all domains except general health) and highly correlated with improvements observed using disease-specific OA outcome measures such as the Western Ontario and McMaster Universities Osteoarthritis Index-visual Analog 3.0 OA index pain and physical function subscales. The effect of rofecoxib vs placebo treatment on mental health largely disappeared after adjustment for improvement in OA disease-specific measures. CONCLUSIONS: Rofecoxib treatment increased physical and mental HRQL domain scores on the SF-36. Improvements in mental health with rofecoxib use primarily resulted from effective treatment of OA (i.e., reduction in pain and improvement in physical function).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".