Videolaryngoscope for Teaching Neonatal Endotracheal Intubation: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To assess whether the videolaryngoscope (VL) is superior to the classic laryngoscope (CL) in acquiring skill in neonatal endotracheal intubation (ETI) and, once acquired with the VL, whether the skill is transferable to the CL. METHODS: This randomized controlled trial, in a level 3 Canadian hospital, recruited junior pediatric residents who performed ETI in the NICU. The primary outcome was success rate of ETI. Secondary outcomes were time to successful intubation, number of bradycardia episodes andlowest oxygen saturation during procedure, occurrence of mucosal trauma, reason for ETI failure, and recognition of problems related to ETI bysupervisor andresident. RESULTS: In phase 1, 34 pediatric residents performed 213 ETIs by using either VL or CL. Intervention groups were comparable at baseline. The success rate was higher (75.2% vs 63.4%, P = .03), and time to successful intubation was longer, inVL group (57 vs 47 seconds, P = .008). In phase 2, 23 residents performed 55 ETIs using CL. The success rate of residents inVL group performing ETI by using the CL was 63% (compared with 75% in phase 1, P = .16). CONCLUSIONS: When learning ETI, the success rate is improved with the VL. Time to successful intubation is longer, but the difference is not clinically significant. When switched to the CL, residents' success rate slightly decreased, but not significantly. This suggests that residents retain a certain level of ETI skill when switched to the CL. The VL is a promising tool for teaching neonatal ETI.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 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".