Use of NSAIDs, COX‐2 inhibitors, and acetaminophen and associated coprescriptions of gastroprotective agents in an elderly population
Bibliographic record
Abstract
OBJECTIVES: To identify determinants of cyclooxygenase 2 (COX-2) inhibitor versus nonsteroidal antiinflammatory drug (NSAIDs) or acetaminophen prescription in seniors; and to compare gastroprotective agent coprescriptions. METHODS: Administrative medical records were obtained from the government of Quebec health insurance agency. Three cohorts were formed based on prescriptions at study entry: COX-2 inhibitors, NSAIDs, or acetaminophen. Logistic regressions were used to adjust for potential confounders. RESULTS: We identified 42,267 patients taking COX-2 inhibitors, 8,235 taking NSAIDs, and 19,716 taking acetaminophen. Determinants of utilizing COX-2 inhibitors versus NSAIDs and versus acetaminophen, respectively, include: female gender (odds ratio [OR] 1.47; 95% confidence interval [95% CI] 1.39-1.55 and OR 1.17; 95% CI 1.12-1.22); musculoskeletal diseases (OR 1.87; 95% CI 1.76-2.00 and OR 2.20; 95% CI 2.10-2.31); and prior gastrointestinal hospitalization (OR 1.82; 95% CI 1.19-2.78 and OR 0.77; 95% CI 0.64-0.92). Gastroprotective agent coprescriptions were lower with COX-2 inhibitors than NSAIDs: OR 0.53; 95% CI 0.48-0.58. CONCLUSIONS: COX-2 inhibitors were more commonly used than NSAIDs in patients with musculoskeletal diseases and those with prior gastrointestinal hospitalizations. Adjusted odds ratios showed a 47% decrease in gastroprotective agent coprescriptions with COX-2 inhibitors compared with NSAIDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".