Effect of cardiovascular comorbidities and concomitant aspirin use on selection of cyclooxygenase inhibitor among rheumatologists
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of cardiovascular comorbidities and aspirin coprescription on cyclooxygenase (COX)-2 inhibitor (coxib) prescribing patterns among rheumatologists. METHODS: A prospective cohort study was carried out with rheumatoid arthritis and osteoarthritis patients in the Consortium of Rheumatology Researchers of North America registry. Medication and comorbidity data were obtained prospectively from physician and patient questionnaires between March 2002 and September 2003. Multivariate adjusted associations between coxib use and specific cardiovascular variables, including aspirin use, were examined. RESULTS: A total of 3,522 arthritis patients were included. COX inhibitors, including coxibs, nonselective nonsteroidal antiinflammatory drugs (NSAIDs), and meloxicam, were prescribed to a larger proportion of osteoarthritis patients (68.4%) than rheumatoid arthritis patients (47.1%) in our study (P < 0.001). COX inhibitors were prescribed to the majority of aspirin users (51.5%) and a similar proportion of nonusers (49.8%). In multivariate analyses, independent predictors of coxib use versus nonselective NSAID use included diagnoses of osteoarthritis (odds ratio [OR] 2.52, 95% confidence interval [95% CI] 1.81-3.52) and diabetes (OR 1.63, 95% CI 1.06-2.51). Conversely, aspirin use independently predicted selection of a nonselective NSAID rather than a coxib (OR 0.73, 95% CI 0.55-0.98). Neither a history of myocardial infarction nor stroke predicted utilization of a coxib. Similarly, cardiovascular variables did not predict the use of rofecoxib versus celecoxib. CONCLUSION: Our data indicate that COX inhibitor coprescription among aspirin users is frequent. Despite cardiovascular concerns regarding the coxibs, our data suggest that aspirin use, but not cardiovascular comorbidities, predicted the selection of nonselective NSAIDs over coxibs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".