Does ECG monitoring affect resuscitation for neonates with pulseless electrical activity in the delivery room? A simulated, pilot, crossover randomised trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether ECG monitoring impacts resuscitative steps during simulated neonatal resuscitation in the setting of pulseless electrical activity (PEA) in the delivery room. DESIGN: This pilot, crossover randomised controlled trial recruited providers in teams of three who participated in two simulation scenarios (PEA with and without ECG monitoring). Teams were randomised to one scenario and then crossed over. All sessions were video-recorded. The primary outcome was time to pulse check once the manikin was programmed to become pulseless. The secondary outcomes were total pulse checks, time to positive pressure ventilation, intubation, chest compressions and administration of epinephrine, and teams' quotes and behaviours during resuscitation. The primary outcome was analysed using Kaplan-Meier survival curve. The secondary outcomes were compared with Wilcoxon signed-rank test. The quotes were analysed using content analysis with pattern coding. RESULTS: Eighty-two healthcare providers were approached and 30 consented (10 teams). The mean time to check the pulse once the manikin was pulseless was 38.5 s (SD 30.1) without ECG vs 88.1 s (SD 46.1) with ECG (p<0.01). There was a significantly decreased number of pulse checks with the ECG compared with without (p<0.01). Time to intubation, chest compressions, start of positive pressure ventilation and epinephrine administration was not different between the groups. Quotes/behaviours revealed false reassurance and over-reliance on ECG monitoring, repeated pulse check errors and troubleshooting behaviours. CONCLUSIONS: ECG monitoring in simulated neonatal resuscitation results in delayed recognition of a pulseless state, decreased number of pulse checks and a possible false sense of security. Simulated resuscitation clinical endpoints are unaffected.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".