Learning Neonatal Intubation Using the Videolaryngoscope
Bibliographic record
Abstract
INTRODUCTION: The use of the videolaryngoscope (VL) facilitates intubation in adults and children, but experience in neonates is scarce. The objective of this study was to compare the VL with the classic laryngoscope (CL) in acquiring the skill of neonatal endotracheal intubation (ETI) and evaluate transferability of skill from VL to CL. We hypothesize that, on a neonatal mannequin, the VL will be superior to the CL with regard to success rate and that the skill will be transferred from VL to CL. METHODS: A randomized controlled trial was held at Sainte-Justine Hospital's simulation center. Third- and fourth-year medical students were randomized into group A, which used VL for the first phase and CL for the second phase, and group B, which used CL for both phases. Each subject performed 9 ETI on 3 simulated neonatal airways in each phase. RESULTS: Thirty-four students performed 612 intubations. Success in group A was higher than in group B in the first phase of the study (96.5% vs. 84.6%, P < 0.001). During phase 2, group A's success did not change significantly (91.7% vs. 96.5%, P = 0.07). Time to successful intubation was longer using the VL (27.6 vs. 15.6 seconds, P < 0.001), but there was no difference in phase 2 (12.5 vs. 10.2 seconds, P = 0.24). There were no esophageal intubations using the VL. CONCLUSIONS: Success rate of ETI on mannequins was improved, and esophageal intubations decreased while learning ETI using the VL compared with the CL. Once ETI is learned on mannequins using the VL, this skill is transferrable to the CL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".