Channelling of COX‐2 inhibitors to patients at higher gastrointestinal risk but not at lower cardiovascular risk: the Cox2 inhibitors and tNSAIDs description of users (CADEUS) study
Bibliographic record
Abstract
PURPOSE: To describe the characteristics of users of cyclo-oxygenase (COX)-2 inhibitors and traditional nonselective non-steroidal anti-inflammatory drugs (tNSAIDs) in France. METHODS: Between 1 August 2003 and 31 July 2004, patients who received at least one dispensing of celecoxib, rofecoxib or tNSAIDs were randomly sampled with a 1:1:2 target ratio within the French National Healthcare Insurance database. Patients and prescribers were asked to fill a questionnaire on socio-demographic characteristics, NSAID indication and use and previous medical history. For each respondent, healthcare resources used in the 6 months before inclusion were extracted from the database. Multivariate logistic regression was used to study the determinants of a first COX-2 inhibitor dispensing. RESULTS: Of the 45 217 patients included, 13 065 COX-2 inhibitors and 13 553 tNSAID users had prescriber data. Ninety seven per cent of COX-2 inhibitor prescriptions were for 'rheumatological' indications, whereas 37% of tNSAIDs use was for benign diseases (n = 2643) or analgesia (n = 2318). Among patients with rheumatological indications (n = 4730) and a first COX-2 inhibitor (n = 2427) or tNSAID (n = 2303) dispensing, multivariate analysis of factors associated with COX-2 inhibitors dispensing showed that, compared to new tNSAID users, new COX-2 inhibitor users were older, more often female, on sick leave or unemployed. COX-2 use was also associated with previous gastrointestinal history and previous gastroprotective agent dispensing, but not with previous cardiovascular (CV) history. CONCLUSION: The choice of NSAID depended largely on indication and on previous gastrointestinal history, in line with the recommendations of the French health authorities. Possible knowledge of CV risk associated with COX-2 inhibitors did not influence prescribing.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".