The Association Between Emergency Department Crowding and the Disposition of Patients With Transient Ischemic Attack or Minor Stroke
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) crowding has been associated with adverse events, including short-term death and hospitalization among discharged patients. The mechanisms are poorly understood, but may include altered physician decision-making about ED discharge of higher-risk patients. One example is patients with transient ischemic attack (TIA) and minor stroke, who are at high risk of subsequent stroke. While hospitalization is frequently recommended, little consensus exists on which patients require admission. OBJECTIVES: The authors sought to determine the association of ED crowding with the disposition of patients with minor stroke or TIA. METHODS: This was a retrospective cohort study of prospectively collected data from the Registry of the Canadian Stroke Network at 12 EDs in Ontario, Canada, between 2003 and 2008, linked to administrative health databases. A hierarchical logistic regression model was used to determine the association between crowding at the time the patient was seen in the ED (defined as mean ED length of stay) and patient disposition (admission/discharge), after adjusting for patient and hospital-level variables. RESULTS: The study cohort included 9,759 patients (4,607 with TIA and 5,152 with minor stroke); 49.5% were discharged from the ED. The mean (±SD) age of study patients was 70.78 (±13.40) years, with 52.9% being male, 37.3% arriving by emergency medical services, and 92.3% triaged as emergent or urgent. Greater severity of ED crowding was associated with a lower likelihood of discharge, regardless of ED size. CONCLUSIONS: These results suggest that crowding may influence clinical decision-making in the disposition of patients with TIA or minor stroke and that, as crowding worsens, the likelihood of hospitalization increases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".