Repeat adverse drug events associated with outpatient medications: a descriptive analysis of 3 observational studies in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Adverse drug events are an important cause of preventable emergency department visits and hospital admissions. We examined repeat adverse drug events associated with outpatient medications resulting in acute care utilization. METHODS: This descriptive analysis combined data from 3 prospective multicentre observational studies, in which clinical pharmacists and physicians independently evaluated patients who visited the emergency department for adverse drug events in 3 hospitals in British Columbia. During these studies, an independent committee adjudicated all discordant and uncertain cases using a standardized algorithm. For the current study, we retrospectively reviewed the medical and research records of all patients 19 years of age and older who had been diagnosed with an adverse drug event during the primary studies to determine the proportion of repeat events. We used multivariable logistic regression to identify factors associated with repeat events; we adjusted for clustering at the hospital level for patient-level analyses and at the patient level for event-level analyses. RESULTS: Among 12 977 patients, 1178 were diagnosed with 1296 adverse drug events at the point of care. Of these events, 32.5% (421 of 1296; 95% confidence interval [CI] 29.8%-35.1%) were repeat events, of which 75.3% (317 of 421; 95% CI 71.1%-79.5%) were deemed probably or definitely preventable as re-exposure to the culprit medication or repeat withdrawal of an indicated medication was inconsistent with best medical practice. Patients presenting with repeat events were more likely to have renal failure (odds ratio [OR] 2.01; 95% CI 1.32%-3.07%) or a mental health diagnosis (OR 1.39; 95% CI 1.02%-1.88%). INTERPRETATION: A high proportion of adverse drug events were repeat events, most of which were deemed preventable. Interventions to ensure that care providers are aware of previously diagnosed adverse drug events when prescribing or dispensing need to be developed and evaluated and may reduce unintentional re-exposures to previously harmful medications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".