A chart review tool to systematically assess the safety of prehospital care for children with out‐of‐hospital cardiac arrest
Bibliographic record
Abstract
Objective: Create an easy-to-use pediatric out-of-hospital cardiac arrest (OHCA)-specific chart review tool to reliably detect severe adverse safety events (ASEs) in the prehospital care of children with OHCA. Methods: We revised our previously validated pediatric prehospital adverse event detection system (PEDS) tool, used to evaluate ASEs in the prehospital care of children during emergent calls, to create an OHCA-specific chart review tool. We developed decision support for reviewers, reviewer training, and a dedicated section for chart data abstraction. We randomly selected 28 charts for independent review by 2 expert reviewers who determined the presence or absence of a severe ASE for each care episode and identified the domain of care and preventability for each ASE. We calculated inter-rater agreement in the assessment of the presence or absence of a severe ASE using Gwet's first-order agreement coefficient (AC1). Results: The PEDS-OHCA chart review tool has 6 sections, with a minimum of 70 and maximum of 667 total possible fields. We found inter-rater agreement of 0.83 (95% confidence interval, 0.63-0.99) between our 2 reviewers for the overall detection of a severe ASE and an average time to complete of 8 minutes (range, 2-25 minutes). Inter-rater agreement in the detection of a severe ASE in each individual domain ranged from 0.36 to 0.96. Conclusions: The PEDS-OHCA is the first chart review tool to systematically evaluate the safety and quality of EMS care for children with OHCA. This tool may help improve understanding of the quality of EMS care for children with OHCA, which is essential to improving 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 | 0.062 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".