Is Video Laryngoscopy the Optimal Tool for Successful Intubation in a Neonatal Simulation Setting? A Single-Center Experience
Bibliographic record
Abstract
Abstract Background Endotracheal intubation is a skill required for resuscitation. Due to various reasons, intubation opportunities are decreasing for health care providers. Objective To compare the success rate of video laryngoscopy (VL) and direct laryngoscopy (DL) for interprofessional neonatal intubation skills in a simulated setting. Methods This was a prospective nonrandomized simulation crossover trial. Twenty-six participants were divided into three groups based on their frequency of intubation. Group 1 included pediatric residents; group 2 respiratory therapists and transport nurses; and group 3 neonatal nurse practitioners and physicians working in neonatology. We compared intubation success rate, intubation time, and laryngoscope preference. Results Success rates were 100% for both DL and VL in groups 1 and 2, and 88.9% for DL and 100% for VL in group 3. Median intubation times for DL and VL were 22 seconds (interquartile range [IQR] 14.3–22.8 seconds) and 12.5 seconds (IQR 10.3–38.8 seconds) in group 1 (p = 0.779); 17 seconds (IQR 8–21 seconds) and 12 seconds (IQR 9–16.5 seconds) in group 2 (p = 0.476); and 11 seconds (IQR 7.5–15.5 seconds) and 15 seconds (IQR 11.5–36 seconds) in group 3 (p = 0.024). Conclusion We conclude that novice providers tend to perform better with VL, while more experienced providers perform better with DL. In this era of decreased clinical training opportunities, VL may serve as a useful tool to teach residents and other novice health care providers.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".