Trends in the coprescription of proton pump inhibitors with clopidogrel: an ecological analysis
Bibliographic record
Abstract
BACKGROUND: In early 2009, 2 observational studies and a US Food and Drug Administration (FDA) advisory addressed the drug interaction between proton pump inhibitors (PPIs) and clopidogrel. One study suggested that pantoprazole could be used safely in this setting, whereas the other study and the FDA advisory did not distinguish among PPIs. We examined trends in PPI prescribing among clopidogrel recipients in the period following these events. METHODS: We conducted a population-based time series analysis of Ontario residents aged 66 years or older for whom clopidogrel was prescribed between Apr. 1, 1999, and Sept. 30, 2013. We determined the proportion of clopidogrel recipients dispensed a PPI during each quarter and the proportions who received pantoprazole or other PPIs. The outcome of interest was change in the use of pantoprazole. RESULTS: In the final quarter of 2008, pantoprazole represented 23.7% of all PPI prescriptions dispensed to patients receiving clopidogrel. Following the publications and FDA advisory in early 2009, pantoprazole use increased substantially. By the end of 2009, this medication accounted for 52.5% of all PPI prescriptions issued to patients receiving clopidogrel; by the end of the study period, it accounted for 71.0% of all PPI prescriptions dispensed to such patients (p < 0. 001). We also observed a modest drop in overall PPI use among clopidogrel recipients beginning in early 2009. INTERPRETATION: In 2009, the prescribing of PPIs with clopidogrel changed substantially in Ontario, with pantoprazole rapidly becoming the most commonly prescribed agent in its class. However, a modest decline in overall PPI use also occurred that may reflect suboptimal translation of emerging drug safety information to clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".