The Use of Automated External Defibrillators in Infants
Bibliographic record
Abstract
OBJECTIVE: 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. The Scientific Advisory Council of the American Red Cross reviewed the literature on the use of AEDs in infants in order to make recommendations on use in the population. METHODS: The Cochrane library and PubMed were searched for studies that included AEDs in infants, any external defibrillation in infants, and simulation studies of algorithms used by AEDs on pediatric arrhythmias. RESULTS: There were 4 studies on the accuracy of AEDs in recognizing pediatric arrhythmias. 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 algorithms used by AEDs had high sensitivity and specificity for pediatric arrhythmias and very rarely recommended a shock inappropriately. The energy doses delivered by AEDs were high, although in the range that have been used in out-of-hospital arrest. In addition, there are data to suggest that 2 to 4 J/kg may not be effective defibrillation doses for many children. CONCLUSIONS: In the absence of prompt defibrillation for ventricular fibrillation or pulseless ventricular tachycardia, survival is unlikely. Automated external defibrillators should be used in infants with suspected cardiac arrest, if a manual defibrillator with a trained rescuer is not immediately available. Automated external defibrillators that attenuate the energy dose (eg, via application of pediatric pads) are recommended for infants. If an AED with pediatric pads is not available, the AED with adult pads should be used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".