Exposing medical students to various difficulty levels of simulated endotracheal intubations improves success rate: a randomised non-blinded trial
Bibliographic record
Abstract
Objective: Simulation training of endotracheal intubation (ETI) has proven to be an effective training tool. We used an adjustable airway mannequin that allows the achievement of various difficulty levels of laryngoscopy to train inexperienced medical students. The purpose of this study was to evaluate the effect of training using this novel airway mannequin on ETI success rates of medical students. Methods: This was a randomised non-blinded trial conducted at the Steinberg Centre for Simulation and Interactive Learning. Twenty recruited medical students were randomly allocated to two different training groups. During training, the mixed training group was asked to perform successful intubations in three levels of difficulty; the standard training group was asked to perform the same number of successful intubations in one level of difficulty. After training, all participants were asked to perform intubations using both the adjustable airway mannequin and a standard mannequin. Success rates and airway surface area visualised were compared between the two groups. Results: Students in the mixed training group had a significantly higher success rate both in the adjustable airway mannequin (p=0.01) and in the standard mannequin (p=0.02). Students in the mixed group had 51%, 59% and 47% significantly more visual area surface than students in the standard group during standard and difficult setup of the adjustable airway mannequin and the standard airway mannequin, respectively. Conclusions: The use of an adjustable airway mannequin to train medical students leads to superior ETI success rates and better glottis visualisation.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".