Re‐Exposure to Culprit Medication Following Adverse Drug Event Diagnosis in Canadian Emergency Department Patients: A Cohort Study
Bibliographic record
Abstract
PURPOSE: The magnitude of repeat exposures to culprit medications after hospital discharge is not well studied. We combined prospective cohort data with administrative health data to understand the frequency of repeat exposures to culprit medications after discharge and the risk factors for their occurrence. METHODS: This was a retrospective analysis of three prospective cohorts of patients who presented to the hospital with an adverse drug event in British Columbia, from 2008 to 2015 (n = 849). We linked prospectively identified adverse drug events to administrative data to examine patterns of redispensing of culprit medications. We used Cox regression to assess risk factors for re-exposure, and conducted subgroup analyses for essential vs. nonessential medications. RESULTS: Among 849 diagnosed adverse drug events, 45.2% had subsequent culprit medication redispensing within a year of hospital discharge. The factors associated with re-exposures included atrial fibrillation, adverse drug event type (e.g. adverse reaction), culprit medication type, and longer historical duration of medication use. CONCLUSIONS: Re-exposures to culprit medications occurred in almost half of the adverse drug events diagnosed in emergency departments. Many of these were appropriate re-exposures to essential medications for indications in which the risk of uncontrolled disease likely outweighed the risk of a repeat adverse event. More research is needed to understand re-exposures to nonessential medications or medications with safer alternatives.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".