Therapy switching and associated costs in elderly patients receiving COX-2 selective inhibitors or non-selective non-steroidal anti-inflammatory drugs in Quebec, Canada
Bibliographic record
Abstract
OBJECTIVES: Lack of efficacy or tolerability of some non-steroidal anti-inflammatory drugs (NSAIDs) may lead to switching between non-selective NSAIDs (nsNSAIDs) and cyclooxygenase-2 (COX-2) selective inhibitors (coxibs), potentially increasing treatment costs due to additional physician visits and wastage of medication. This study assessed drug switching and associated costs among elderly chronic NSAID users. METHODS: Data for patients who filled their first prescription for a coxib or nsNSAID in 2001 were obtained from the Quebec Health Insurance Agency. Follow-up was terminated at the earliest of: 1 yr, the first day without NSAID exposure following the index filling date, or death. Patients could switch NSAIDs several times during follow-up. Person-days of exposure were categorized by the NSAID most recently dispensed: rofecoxib, celecoxib, Arthrotec(R) or non-Arthrotec (nA) nsNSAID. Cox regression models compared time to switch between groups, adjusting for patient baseline characteristics. Upon a switch, pills remaining from the previous prescription were considered wasted. The costs of wasted pills and switch-associated physician visits were estimated. RESULTS: Throughout follow-up, patients filled 38 267 prescriptions for rofecoxib, 31 282 for celecoxib, 1108 for Arthrotec and 4388 for nA-nsNSAIDs. Adjusted hazard ratios (95% confidence interval) for switching versus nA-nsNSAIDs were: rofecoxib, 0.39 (0.35-0.44); celecoxib, 0.43 (0.38-0.48). Compared with nA-nsNSAID prescriptions, adjusted switching-related healthcare costs were 53 and 47% lower on average for rofecoxib and celecoxib prescriptions, respectively. These costs were 34% higher for Arthrotec prescriptions than for nA-nsNSAIDs. CONCLUSIONS: Compared with recipients of nsNSAIDs, coxib recipients were less likely to switch medications and had approximately half the adjusted costs for switching-related wasted resources per prescription.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".