Prospective Validation of Clinical Criteria to Identify Emergency Department Patients at High Risk for Adverse Drug Events
Bibliographic record
Abstract
OBJECTIVES: Adverse drug events (ADEs) cause or contribute to one in nine emergency department (ED) presentations in North America and are often misdiagnosed. EDs have insufficient clinical pharmacists to complete medication reviews in all incoming patients, even though pharmacist-led medications reviews have been associated with improved health outcomes. Our objective was to validate clinical decision rules to identify patients presenting with ADEs so they could be prioritized for pharmacist-led medication review. METHODS: This multicenter, prospective study was conducted in two tertiary and one community hospital in Canada. We enrolled 1,529 adults presenting to EDs over 12 months. We applied two clinical decision rules and collected baseline variables prior to assessments by clinical pharmacists and physicians. We compared the physician and pharmacist diagnoses with the decision rule results. The primary outcome was a moderate or severe ADE, defined as an unintended and harmful event related to medication use or misuse, which required a change in medical therapy, diagnostic testing, consultation, or admission. An independent committee adjudicated uncertain and discordant cases. We calculated the diagnostic accuracy of both rules. RESULTS: Among 1,529 patients, 184 (12.0%) were diagnosed with an ADE. Rule 1 contained the variables 1) having a preexisting medical condition or having taken antibiotics within 1 week and 2) age > 80 years or having a medication change within 28 days. They had a sensitivity of 91.3% (95% confidence interval [CI] = 86.3%-95.0%) and a specificity of 37.9% (95% CI = 35.3%-40.6%) for ADEs. CONCLUSIONS: Our study validated clinical decision rules that can be applied by clinical pharmacists to limit the number of patients requiring medication review, while identifying the majority of patients presenting with clinically significant ADEs.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".