Abstract 233: What Is the Evidence for the Use of Automated External Defibrillators in Infants?
Bibliographic record
Abstract
Background: Automated external defibrillators (AEDs) have been used successfully in many populations to improve survival for out-of-hospital cardiac arrest. While ventricular fibrillation and pulseless ventricular tachycardia are more prevalent in adults, these arrhythmias do occur in infants. Historically, there has been controversy about the safety and efficacy of AEDs in this population. Therefore, we conducted a systematic review on the use of AEDs in infants. Methods: The Cochrane library and Pubmed were searched for relevant studies that included automated external defibrillators in infants, any external defibrillation in infants, and simulation studies of algorithms used by AEDs on pediatric arrhythmias. Results: There were four studies on the accuracy of AEDs in recognizing pediatric arrhythmias. The algorithms used by AEDs had high sensitivity and specificity for pediatric arrhythmias and very rarely recommended a shock inappropriately. Case reports (n=2) demonstrated successful use of AED in infants and a retrospective review (n=1) of pediatric pads for AEDs included infants. Six studies addressed defibrillation dosages used. The energy doses delivered by AEDs were high, though in the range that have been used in out-of-hospital arrest. Additionally, there were data to suggest that 2-4 J/kg may not be effective defibrillation doses for many children. Conclusions: There are limited data from clinical studies on AED use if infants and no randomized clinical trials. However, available evidence suggests that AEDs are safe and effective in this population. As survival is unlikely in the absence of prompt defibrillation for ventricular fibrillation or pulseless ventricular tachycardia, AEDs should be used in infants with suspected 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 | 0.013 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".