The Level of Knowledge of Anesthesiologist Residents About Difficult Airway Management
Bibliographic record
Abstract
Background: Airway management continues to be one of the main challenges for anesthesiologists, so it is essential that these professionals know about the anatomy and functioning of the airways since the beginning of their residency, not only for the safe application of anesthesia, but also for avoid unwanted complications with risk of permanent sequelae and even death. The aim of this study was to assess the degree of knowledge of anesthesiology residents in handling difficult airways. Methods: The prospective comparative study was carried out at the Hospital de Clinicas Municipal of Sao Bernardo do Campo, belonging to the Brazilian Health System (SUS) recently accredited by the Canadian company Qmentum, of among students of three years of medical residency in anesthesiology of both sexes, with the application of a printed multiple-choice questionnaire on difficult airway management, carried out in January 2021. For statistical analysis it was using Microsoft Excel spreadsheet and R Commander from software R version 4.1.0. We used the literature standard p-value as significance to be 0.05. Results: Fifteen residents were interviewed (6 R1, 5 R2 and 4 R3) with an average age of 31 years. An increase in the learning of residents after three years was noticed in the following aspects: anatomy of the difficult airway management, Mallampati and Cormack classification, supraglottic devices, difficult airway management case equipment and difficult airway anagement algorithm. On the other hand, the predictors of difficult airway management and difficult ventilation under mask obtained a lower-than-expected result. In general, there was a better performance and confidence in those who have already participated in some difficult airway training. Conclusion: This study allowed us to assess that at the end of the residency, students' learning increased significantly, as well as their confidence, especially in those who had already done some training in difficult airway management, such data point to the importance of investing in training on the topic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".