P2763Factors influencing ambulance use in patients with suspected acute coronary syndromes: a population-based geographic information system study
Bibliographic record
Abstract
Background: Despite all public awareness campaigns and guideline recommendations, the majority of patients with symptoms suggestive of acute coronary syndrome (ACS) do not use emergency medical services (EMS) to reach the emergency department (ED). Purpose: The aim of this study was to investigate the factors associated with EMS utilization and subsequent patient outcomes. Methods: We used data from the metropolitan areas of Edmonton and Calgary, which are of similar size and in the same public health system (population ∼3 million people). Using administrative health databases, all patients who presented to an ED in the years of 2007–2013 with main ED diagnosis of ACS, stable angina or chest pain were included. The travel distance was estimated using the geographic information system method to approximate distance between ED and patient home. The clinical endpoints of interest were the 7-day and 30-day all-cause events (composite of death, re-hospitalization, and repeat ED visit). Results: The cohort consisted of 50,881 patients, 15,553 (30.5%) of which were presented via EMS. The overall rate of EMS utilization was lower in Edmonton compared to Calgary (24.2% vs 36.2%; p<0.0001). Based on multivariate analysis, patients with older age, female sex, ED diagnosis of ACS and stable angina (as compared to chest pain), more comorbidities, with longer travel distance and lower household income were more likely to use EMS to reach the hospital. After adjustment for covariates and with propensity analysis/IPW, EMS use was associated with higher risk of 7-day (OR=1.17, 95% CI 1.09–1.25) and 30-day (OR=1.20, 95% CI 1.13–1.27) clinical 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".