Retrospective chart review and survey to identify adverse safety events in the emergency medical services care of children with out-of-hospital cardiac arrest in the USA: a study protocol
Bibliographic record
Abstract
INTRODUCTION: Efforts to improve the quality of emergency medical services (EMS) care for adults with out-of-hospital cardiac arrest (OHCA) have led to improved survival over time. Similar improvements have not been observed for children with OHCA, who may be at increased risk for preventable adverse safety events during prehospital care. The purpose of this study is to identify patient and organisational factors that are associated with adverse safety events during the EMS care of paediatric OHCA. METHODS AND ANALYSIS: This is a large multisite EMS study in the USA consisting of chart reviews and agency surveys to measure, characterise and evaluate predictors of our primary outcome severe adverse safety events in paediatric OHCA. Using the previously validated Paediatric prehospital adverse Event Detection System tool, we will review EMS charts for 1500 children with OHCA from 2013 to 2019 to collect details of each case and identify severe adverse safety events (ASEs). Cases will be drawn from over 40 EMS agencies in at least five states in geographically diverse areas of the USA. EMS agencies providing charts will also be invited to complete an agency survey to capture organisational characteristics. We will describe the frequency and proportion of severe ASEs in paediatric OHCA across geographic regions and clinical domains, and identify patient and EMS organisational characteristics associated with severe ASEs using logistic regression. ETHICS AND DISSEMINATION: This study has been approved by the Oregon Health & Science University Institutional Review Board (IRB Approval# 00018748). Study results will be disseminated through scientific publications and presentations, and to EMS leaders and staff through local EMS medical directors, quality and training officers and community engagement activities.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".