Abstract 326: Epidemiology Of Pediatric Out-of-hospital Cardiac Arrest In Ontario, Canada
Bibliographic record
Abstract
Introduction: There are no epidemiological studies of Pediatric Out-of-Hospital Cardiac Arrest (POHCA) in Canada for at least 20 years. Understanding who has a POHCA event is key to prevention, education, and management strategies. Methods: In this retrospective cohort study, we used a validated algorithm applied to large hospital administrative databases to describe the epidemiology of pediatric (age 1 day to < 18 years) OHCA in Ontario, Canada, from 2004-2020, by pre-existing comorbidities, sociodemographic features, and outcomes. Results: The cohort included 1,839 unique pediatric patients with a POHCA event with a median age of 2 (0-12) years and 721 patients (39.2%) less than 1 year. Males accounted for 61.1% (n=1,123) of the cohort. Incidence was 4.2/100,000 with a gradual increase over the study period. Thirty percent (n=560) of the cohort lived in a neighborhood in the lowest income quintile, while 13.6% (n=251) lived in a neighborhood in the highest income quintile. Seventy five percent (n=1,380) lived in an urban setting. Seventy nine percent (n=1,444) first presented to a non-teaching, non-pediatric hospital following the POHCA event. The majority (n=1,533, 83.4%) of patients did not have significant comorbidities prior to the POHCA. The most common comorbidity was congenital cardiac malformation for 147 (8.0%) of patients. Three hundred and thirty-three (18.1%) survived hospital admission, and 132 (7.2%) survived to hospital discharge. Less than 6 patients had a subsequent POHCA in the year following the index event. Conclusions: This is the largest Canadian POHCA cohort and the first to describe incidence, comorbidities, and sociodemographic characteristics for this population. This is the first study to use a validated algorithm to create a POHCA cohort using large hospital administrative databases. We found a gradual increase in annual incidence over 17 years, POHCA mostly occurred in healthy children, and survival was lower than other reported cohorts. There were more than double the number of children with POHCA events living in the lowest income quintile neighborhoods compared to the highest income quintile neighborhoods. Most children with POHCA presented to non-teaching, non-pediatric hospitals first.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".