42: Videolaryngoscope vs Classic Laryngoscope in Teaching Neonatal Endotracheal Intubation: A Randomized Controlled Trial
Bibliographic record
Abstract
For pediatric trainees, acquiring the skill of neonatal endotracheal intubation (ETI) is difficult because of particularities in the neonate's airway. The videolaryngoscope (VL) technique has been used for adult and pediatric airways, and could be a method of choice in teaching neonatal ETI. On the neonatal mannequin model, VL improves success rate of ETI. However, only preliminary clinical experience has been described in newborns. Assess if the VL is superior to the classic laryngoscope (CL) in acquiring neonatal ETI skill in the Neonatal Intensive Care Unit (NICU). A randomized controlled trial was held in the NICU at CHU Ste-Justine (July 2011 to June 2013). Primary outcome: Success rate and learning curve (Generalized estimating equations). Secondary outcomes: a) Time to successful intubation (Mann-Whitney Test); b) Recognition of problems related to ETI by the supervisor and residents' level of confidence (Independent t-test). We randomized 34 pediatric residents to perform 213 ETI using either the VL or the CL. In both groups, prior training, experience in the NICU and with neonatal ETI were similar. Patient characteristics, success rate and time to successful intubation are presented in the table. The learning curve seems better with the CL for the first two intubations, however the initial success rate in this group is lower. After the second intubation, the learning curve is similar for both laryngoscopes. Supervisors recognized problems related to visualization of glottis (P=0.02) and insertion of endotracheal tube (P=0.05) more easily with the VL. Residents' level of confidence regarding their technical competence was higher with the CL (P=0.01). In the NICU, while learning ETI, success rate is improved with the VL. Although time to successful intubation with the VL is longer, this difference is not clinically significant. Residents have a higher level of confidence in their technical competence when using the CL, however supervisors recognize more easily problems in the ETI procedure with the VL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".