Real-Time Intubation and Ventilation Feedback: A Randomized Controlled Simulation Study
Bibliographic record
Abstract
BACKGROUNDS: This study aimed to determine the best educational application of a respiratory function monitor and a video laryngoscope. METHODS: This study was a randomized controlled simulation-based trial, including 167 medical students. Participants had to execute ventilation and intubation maneuvers on a newborn manikin. Participants were randomized into 3 groups. In group A (no-access), the feedback devices were not visible but recording. In group B (supervisor-access), the feedback devices were visible to the supervisor only. In group C (full-access), both the participant and the supervisor had visual access. RESULTS: The two main outcome variables were the percentage of ventilations within the tidal volume target range (4-8mL/kg) and the number of intubation attempts. Group C achieved the highest percentage of ventilations within the tidal volume target range (full-access 63.6%, supervisor-access 51.0%, no-access 31.1%, P < .001) and the lowest mask leakage (full-access 34.9%, supervisor-access 46.6%, no-access 61.6%; A to B: P < .001, A to C: P < .001, B to C: P = .003). Overall, group C achieved superior ventilation quality regarding primary and secondary outcome measures. The number of intubation attempts until success was lowest in the full-access group (full-access: 1.29, supervisor-access: 1.77, no-access: 2.43; A to B: P = .001, A to C: P < .001, B to C: P = .015). CONCLUSIONS: Our findings confirm that direct visual access to feedback devices for supervisor and trainees alike considerably benefits outcomes and can contribute to the future of clinical education.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".