Learning curve and performance in simulated difficult airway for the novel C-MAC® video-stylet and C-MAC® Macintosh video laryngoscope: A prospective randomized manikin trial
Bibliographic record
Abstract
Difficult airways can be managed with a range of devices, with video laryngoscopes (VLs) being the most common. The C-MAC® Video-Stylet (VS; Karl-Storz Germany), a hybrid between a flexible and a rigid intubation endoscope, has been recently introduced. The aim of this study is to investigate the performance of the VS compared to a VL (C-MAC Macintosh blade, Karl-Storz Germany) with regards to the learning curve for each device and its ability to manage a simulated difficult airway manikin. This is a single-center, prospective, randomized, crossover study involving twenty-one anesthesia residents performing intubations on a Bill 1™ (VBM, Germany) airway manikin model. After a standardized introduction, six randomized attempts with VL and VS were performed on the manikin. This was followed by intubation in a simulated difficult airway (cervical collar and inflated tongue) with both devices in a randomized fashion. The primary end-point of this study was the total time to intubation. All continuous variables were expressed as the median [interquartile range] and analyzed using the Mann-Whitney U test. A 2-way ANOVA with Bonferroni's post hoc test was used to compare both devices at each trial. All reported p values are two sided. The median total time to intubation on a simulated difficult airway was faster with the VS compared to VL (17 [13.5-25] sec vs 23 [18.5-26.5] sec, respectively; 95% CI; P = 0.031). Additionally, on a normal airway manikin, the VS has a comparable learning curve to the VL. In this manikin-based study, the novel VS was comparable to the VL in terms of learning curve in a normal airway. In a simulated difficult airway, the total time to intubation, though likely not clinically relevant, was faster with the VS to the VL. However, given the above findings, this study justifies further human clinical trials with the VS to see if similar benefits-faster time to intubation and similar learning curve to VL-are replicated clinically.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".