Preventable adverse drug events: Descriptive epidemiology
Bibliographic record
Abstract
AIM: Our objective was to identify preventable adverse drug events and factors contributing to their development. METHODS: We performed a retrospective chart review combining data from three prospective multicentre observational studies that assessed emergency department patients for adverse drug events. A clinical pharmacist and physician independently reviewed the charts, extracted data and rated the preventability of each adverse drug event. A third reviewer adjudicated all discordant or uncertain cases. We calculated the proportion of adverse drug events that were deemed preventable, performed multivariable logistic regression to explore the characteristics of patients with preventable events, and identified contributing factors. RESULTS: We reviewed the records of 1 356 adverse drug events in 1 234 patients. Raters considered 869 (64.1%) of adverse drug events probably or definitely preventable. Patients with mental health diagnoses (OR 1.8; 95% CI 1.3-2.5) and diabetes (OR 1.7; 95% CI 1.2-2.4) were more likely to present with preventable events. The medications most commonly implicated in preventable events were warfarin (9.4%), hydrochlorothiazide (4.5%), furosemide (4.0%), insulin (3.9%) and acetylsalicylic acid (2.7%). Common contributing factors included inadequate patient instructions, monitoring and follow-up, and reassessments after medication changes had been made. CONCLUSIONS: Our study suggests that patients with mental health conditions and diabetes require close monitoring. Efforts to address the identified contributing factors are needed.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".