Rofecoxib shows consistent efficacy in osteoarthritis clinical trials, regardless of specific patient demographic and disease factors.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of rofecoxib (Vioxx) in subpopulations of patients with osteoarthritis (OA) identified by demographic or baseline disease characteristics, or varied OA involvement. METHODS: Data were combined from three 6-week double blind trials in patients with OA of the knee or hip. All trials contained placebo, 12.5 mg rofecoxib, and 25 mg rofecoxib arms (the only trials to date containing all 3 treatments). Analyses were performed on subgroups categorized according to the following baseline demographics and disease characteristics [age, sex, height, weight, body mass index, American Rheumatism Association (ARA) functional class, joint tenderness, joint stiffness, Western Ontario-McMaster University OA Index (WOMAC) functional subscale, unilateral/bilateral joint involvement, number of joint groups involved]. Three primary endpoints--Pain Walking on Flat Surface (WOMAC), Patient Global Assessment of Response to Therapy, and Investigator Global Assessment of Disease Status--were analyzed. The global assessments, which provided data on overall aspects of OA, regardless of affected joint, were used to assess effects among patients with one, 2, 3, or 4 joint groups affected (from among the following: interphalangeal/first carpal-metacarpal joint, spine, hip, or knee). RESULTS: Data from 1501 patients were included. No consistent treatment-by-subgroup interaction was observed with all 3 primary endpoints for patients taking placebo or 12.5 or 25 mg rofecoxib. Rofecoxib showed generally consistent efficacy across subgroups of patients identified by sex, race, age, OA location(s), prior OA therapy, baseline study joint tenderness or swelling (patients with knee OA only), and ARA functional class level. CONCLUSION: In this combined analysis, no specific factor predicted a differential treatment effect to rofecoxib.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".