Abstract 330: Severe Adverse Safety Events in Pediatric Out-of-Hospital Cardiac Arrest Resuscitation
Bibliographic record
Abstract
Introduction: Survival for children with out-of-hospital cardiac arrest (OHCA) remains poor in spite of improvements in adult OHCA survival. Hypothesis: We hypothesized that severe adverse safety events (ASEs) are common during pediatric OHCA resuscitation and that specific patterns can be identified. Methods: Retrospective cohort study of patient care reports from 51 Emergency Medical Services (EMS) agencies across the US for children less than 18 years of age with an OHCA where resuscitation was attempted by EMS providers between 2013-2019. Results: We evaluated 1,019 encounters of EMS-treated pediatric OHCA; 46% were under 12 months of age. At least one severe ASE occurred in 610 patients (60%), and 310 patients (30%) had two or more. Children under 28 days of age had the highest frequency of ASEs. The most common severe ASEs involved epinephrine administration (30%), ventilation (14%), and vascular access (19%). Logistic regression modeling found that the only factor consistently associated with severe ASEs was young age. Birth-related OHCA had 4-fold greater odds of a severe ASE compared to adolescents (95% CI 1.7-10.7) and neonates with non-birth-related OHCA had 3.2-fold greater odds of a severe ASE (95% CI 1.1-9.0). Conclusions: In this large geographically diverse cohort of children with EMS-treated OHCA, 60% experienced at least one severe ASE. The odds of a severe ASE were over 3-fold higher for neonates than adolescents, and even higher when the cardiac arrest was birth-related. Given the national increase in out of hospital births and ongoing poor outcomes of OHCA in young children, these findings are an important and urgent call to action to focus on improving care delivery and training.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".