Survival Rates After Pediatric Traumatic Out-of-Hospital Cardiac Arrest Suggest an Underappreciated Therapeutic Opportunity
Bibliographic record
Abstract
OBJECTIVES: Children with traumatic arrests represent almost one third of annual pediatric out-of-hospital cardiac arrests (OHCAs). However, traumatic arrests are often excluded from study populations because survival posttraumatic arrest is thought to be negligible. We hypothesized that children treated and transported by emergency medical services (EMS) personnel after traumatic OHCA would have lower survival compared with children treated after medical OHCA. METHODS: We performed a secondary, observational study of children younger than 18 years treated and transported by 78 EMS agencies in southwestern Pennsylvania after OHCA from 2010 to 2014. Etiology was determined as trauma or medical by EMS services. We analyzed patient, cardiac arrest, and resuscitation characteristics and ascertained vital status using the National Death Index. We used multivariable logistic regression to test the association of etiology with mortality after covariate adjustment. RESULTS: Forty eight of 209 children (23%) had traumatic OHCA. Children with trauma were older than those with medical OHCA (13.2 [3.8-15.9] vs 0.5 [0.2-2.4] years, P < 0.001). Prehospital return of spontaneous circulation frequency for trauma versus medical etiology was similar (90% vs 87%, P = 0.84). Patients with trauma had higher mortality (69% vs 45% P = 0.004). CONCLUSIONS: More than 8 of 10 children with EMS treated and transported OHCA achieved return of spontaneous circulation. Despite lower survival rates than medical OHCA patients, almost one third of children with a traumatic etiology survived throughout the study period. Future research programs warrant inclusion of children with traumatic OHCA to improve 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".