Adverse events following an emergency department visit
Bibliographic record
Abstract
BACKGROUND: Many studies demonstrate a high rate of treatment-related adverse outcomes or adverse events. No studies have prospectively evaluated adverse events in patients discharged home from the emergency department (ED). OBJECTIVE: To describe the types of adverse events in patients discharged home from an ED. PATIENTS: PATIENTS who were sent home directly from the ED of an urban, academic teaching hospital in Ottawa, Canada. METHODS: Patient records were reviewed to identify demographic and medical history information. Two weeks following the ED visit, patients completed a standard telephone interview to record post ED visit outcomes. Two physicians reviewed outcomes to identify all adverse events and their cause. RESULTS: Follow-up was complete for 399 of 408 enrolled patients. The median age was 49 years (interquartile range 36-68) and 50% were male. The most common diagnosis was "chest pain", occurring in 74 patients (18%), followed by "bone and joint disorders" in 55 patients (14%). 24 patients experienced an adverse event (incidence 6% (95% CI 4% to 9%)), of which 17 were preventable (incidence 4% (95% CI 3% to 7%)). Five of the unpreventable adverse events were medication side effects and two were minor, procedure-related complications. Of all 24 adverse events, 15 (63%; 95% CI 43 to 79%) led to an additional ED visit or a hospitalisation. Preventable adverse events occurred in 5 of 78 chest pain patients (incidence 6% (95% CI 3% to 14%)). CONCLUSION: Most adverse events occurring following an ED visit are preventable and often relate to diagnostic or management errors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".