Emergency Department Gridlock and Out-of-hospital Delays for Cardiac Patients
Bibliographic record
Abstract
OBJECTIVES: To determine the effect of simultaneous ambulance diversion at multiple emergency departments (EDs) (gridlock) on transport delays for patients with chest pain. METHODS: Retrospective data on consecutive ambulance patients with chest pain and the diversion status of EDs in Toronto were obtained from January 1998 to December 1999. Gridlock was calculated separately for the four city quadrants as the daily duration of episodes where all EDs in the quadrant were simultaneously diverting ambulances. The primary outcome was 90th percentile ambulance transport interval (scene departure to hospital arrival). RESULTS: Eleven thousand four hundred patients were included (mean age 67 years; female 51%; severity of illness: moderate to life-threatening 89%). Gridlock occurred an average of 1.1 hour/day, and 3,060 patients were transported on days when it occurred. Ninetieth percentile transport interval was 15.5 minutes (95% CI = 15.3 to 15.9) for patients not exposed to gridlock vs. 17.4 minutes (95% CI = 16.8 to 17.8) for patients who were exposed to gridlock. In multivariate analyses, gridlock was associated with both transport and total out-of-hospital interval delays (0.2 min/hour, 95% CI = 0.1 to 0.4 and 0.2 min/hour, 95% CI = 0.04 to 0.4, respectively). Delays were similar regardless of patient severity of illness (p = 0.5). Age (0.8 min/10 years, 95% CI = 0.5 to 1), female gender (1.9 min, 95% CI = 1.3 to 2.6), advanced care paramedics (5.3 min, 95% CI = 4.4 to 6.3), and snowfall (0.8 min/cm, 95% CI = 0.2 to 1.5) were also independently associated with delays. CONCLUSIONS: Ambulance diversion was associated with delays in out-of-hospital ambulance transport for chest pain patients, but only when it resulted in gridlock. The magnitude of the out-of-hospital delay was the same regardless of the patient's severity of illness.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".