Emergency Physician Recognition of Adverse Drug-related Events in Elder Patients Presenting to an Emergency Department
Bibliographic record
Abstract
OBJECTIVES: The authors examined the ability of emergency physicians (EPs) to recognize adverse drug-related events (ADREs) in elder patients presenting to the emergency department (ED). METHODS: This was a prospective observational study of patients at least 65 years of age who presented to the ED. ADREs were identified using a validated, standardized scoring system. EP recognition of ADREs was assessed through physician interview and subsequent chart review. RESULTS: A total of 161 patients were enrolled in the study. Thirty-seven ADREs were identified, which occurred in 26 patients (16.2%; 95% confidence interval [CI] = 10.5% to 22.0%). The treating EPs recognized 51.2% (95% CI = 35.2% to 67.4%) of all ADREs. There was better recognition of those ADREs related to the patient's chief complaint (91%; 95% CI = 74.1% to 100%) as compared with recognition of ADREs that were not associated with the chief complaint (32.1%; 95% CI = 14.8% to 49%). EPs recognized six of seven severe ADREs (85.7%), 13 of 23 moderate ADREs (56.5%; 95% CI = 36.8% to 77%), and none of the mild ADREs. Recognition of ADREs varied with medication class. CONCLUSIONS: EP performance was superior at identifying severe ADREs relating to the patients' chief complaints. However, EP performance was suboptimal with respect to identifying ADREs of lower severity, having missed a significant number of ADREs of moderate severity as well as ones unrelated to the patients' chief complaints. ADRE detection methods need to be developed for the ED to aid EPs in detecting those ADREs that are most likely to be missed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".