The Epidemiology of Pediatric Ambulance Transport in Ontario, Canada
Bibliographic record
Abstract
Background:\nTrauma is one of the most frequent requests for ambulance services. However, little is known about the mechanism of injury sustained by pediatric patients seen by a paramedic. There are few studies comparing ambulance transport to private transport in pediatric patients and none within the Canadian context.\n\nMethods:\nThis dissertation consists of three studies that describe the epidemiology of pediatric ambulance transport in Ontario, Canada. Study 1 focuses on creating a profile of children <19 years who were seen by Toronto Paramedic Services. Study 2 examines the difference in all-cause-mortality, hospitalization, ICU admission, and severe injury between ambulance transport and private transport to the emergency department [ED] in children in the province of Ontario. Study 3 examines differences in all-cause-mortality, hospitalization, ICU admission, and severe injury between ambulance transport and private transport in motor vehicle collisions for children in Ontario.\n\nResults:\nIn Manuscript 1, the most common mechanism of injuries were falls, transport related injuries, assault/gunshot injuries, injuries involving choking or foreign bodies, and burn/scald injuries. In Manuscript 2, there were 1,872,519 patients who sustained an injury in Ontario with 129,577 (6.9%) transported to the ED via ambulance. The adjusted OR for all cause mortality was 154.88 (95% CI: 101.11-237.23), the adjusted OR for ICU admission was 19.17 (95% CI: 18.01-20.41), the adjusted OR for hospitalization was 13.27 (95% CI: 12.97-13.57) and the adjusted OR for severe injury was 1.88 (95% CI: 1.78-1.98). In Manuscript 3, There were 32,025 patients who sustained an injury in an MVC with 13,995 (43.7%) transported to the ED via ambulance. The adjusted OR for mortality in an MVC was 33.05 (95% CI: 8.05-135.79), the adjusted OR for ICU admission was 17.14 (95% CI: 11.57-25.39), the adjusted OR for hospitalization was 13.30 (95% CI: 11.05-16.01), and the adjusted OR for severe injury was 2.22 (95% CI: 1.45-3.39).\n\nConclusions:\nThe majority of injuries seen by TPS were fall related, transport-related, gunshots/assaults, choking/foreign body, and burn/scald injuries. Ambulance transport amongst pediatric patients who sustained an injury and patients who were injured in an MVC in Ontario is associated with increased odds of mortality and morbidity outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".