Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To determine whether patients who are not admitted to hospital after attending an emergency department during shifts with long waiting times are at risk for adverse events. DESIGN: Population based retrospective cohort study using health administrative databases. Setting High volume emergency departments in Ontario, Canada, fiscal years 2003-7. PARTICIPANTS: All emergency department patients who were not admitted (seen and discharged; left without being seen). OUTCOME MEASURES: Risk of adverse events (admission to hospital or death within seven days) adjusted for important characteristics of patients, shift, and hospital. RESULTS: 13,934,542 patients were seen and discharged and 617,011 left without being seen. The risk of adverse events increased with the mean length of stay of similar patients in the same shift in the emergency department. For mean length of stay ≥ 6 v <1 hour the adjusted odds ratio (95% confidence interval) was 1.79 (1.24 to 2.59) for death and 1.95 (1.79 to 2.13) for admission in high acuity patients and 1.71 (1.25 to 2.35) for death and 1.66 (1.56 to 1.76) for admission in low acuity patients). Leaving without being seen was not associated with an increase in adverse events at the level of the patient or by annual rates of the hospital. CONCLUSIONS: Presenting to an emergency department during shifts with longer waiting times, reflected in longer mean length of stay, is associated with a greater risk in the short term of death and admission to hospital in patients who are well enough to leave the department. Patients who leave without being seen are not at higher risk of short term adverse events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".