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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".