Use of cognitive aids in paediatric out-of-hospital cardiac arrest
Bibliographic record
Abstract
Background: Paediatric cardiac arrest resuscitation is a high-stakes, rare event that can cause high stress levels in emergency medical services (EMS) practitioners. The demands of this work could raise cognitive load on practitioners, which may contribute to adverse safety events. Methods: An EMS agency survey was developed as part of a larger study into organisation-level factors that affect paediatric out-of-hospital cardiac arrest care. Questions focused on the types and numbers of cognitive aids, and whether a paediatric emergency care coordinator (PECC) was present. The number and frequency of these aids were analysed, and statistical significance assessed. The number and type of aids were stratified according to the presence of a PECC and paediatric call volumes. Results: The number of available resources ranged from 0 to 4, with a mean of 2.6 and a median of 3; the average number used was 2.0. These figures are higher than for adults. The most commonly available resources were local protocols, followed by local medication/equipment guides and Broselow tape. The least commonly available were paediatric advanced life support cards. No significant differences were found between the number of resources and the presence of a PECC or call volume. Conclusion: There is wide variability of resources to support EMS providers in the resuscitation of infants and children in out-of-hospital cardiac arrest.
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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 | 0.018 |
| 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) | 0.002 | 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".