Evaluation of hospital management of paediatric out-of-hospital cardiac arrest
Bibliographic record
Abstract
Introduction: Pediatric out of hospital cardiac arrest (POHCA) is rare, with high mortality and neurological morbidity. Adherence to Pediatric Advanced Life Support guidelines standardizes in-hospital care and improves outcomes. We hypothesized that in-hospital care of POHCA patients was variable and deviations from guidelines were associated with higher mortality. Methods: POHCA patients in the London-Middlesex region between January 2012 and June 2020 were included. The care of children with ongoing arrest (intra-arrest) and post-arrest outcomes were reviewed using the Children's Hospital, London Health Sciences Centre (LHSC) patient database and the Adverse Event Management System. Results: 50 POHCA patients arrived to hospital, with 15 (30%) patients admitted and 2 (4.0%) surviving to discharge, both with poor neurological outcomes and no improvement at 90 days. Deviations occurred at every event with intra-arrest care deviations occurring mostly in medication delivery and defibrillation (98%). Post-arrest deviations occurred mostly in temperature monitoring (60%). Data missingness was 15.9% in the intra-arrest and 1.7% in the post-arrest group. Discussion: Deviations commonly occurred in both in-hospital arrest and post-arrest care. The study was under-powered to identify associations between DEVs and outcomes. Future work includes addressing specific deviations in intra-arrest and post-arrest care of POHCA patients and standardizing electronic documentation.
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 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".