Description of Ambulance Diversions in the Edmonton Region
Bibliographic record
Abstract
Abstract Background: Diversion of ambulances by hospital emergency departments has become a day-to-day occurrence in many jurisdictions within Canada. Yet, despite the increasing prevalence of this phenomenon, its impact on transported patients, on the EMS system, and on the health care system overall has not, to date, been well quantified. Despite the increasing sophistication and capabilities of North American EMS systems, it is difficult to argue with the principle that unstable or potentially unstable patients are best served by expeditious transport for definitive care to acute care facilities. T o this end, this study represents an effort to assess the systemic and patient care impacts of ambulance diversions. Methods: Patient-care and corresponding ambulance trip records for all patients transported by this EMS system for a five week period were abstracted to identify those patients in which an ambulance was diverted from its initial destination. Adverse events include hypotensive episodes, airway compromise, changes in level of consciousness, and the onset of violent behavior. Response and transport times also were abstracted, comparisons utilized student's t-test and 95% Confidence Intervals. Results: Ambulance diversions increased EMS response times and prehospital transport times. Adverse medical events occurred during 4.3% of diverted ambulance runs. Patients, when faced with the prospect of transport to other than their hospital of choice, not infrequently cancelled EMS transport and sought other means of transport. Subsequent interfacility transport was required for 4.3% of the diverted patients. Conclusions: Diversion of ambulances impacts the EMS system by increasing response and transport times; the region, by generating subsequent interfacility transports; and patients, as adverse medical events can occur during the diverted transport.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".