Learning awake fiberoptic intubation: use of a computerized mannequin simulator teaching
Bibliographic record
Abstract
Background: Nasotracheal intubation is the most common method used to ventilate patients for oral maxillofacial surgeries. Fiberoptic intubation (FOI) is an essential skill for a wide range of healthcare providers, including anesthesiologists and emergency medicine physicians. However, proper programs and facilities for intubation training are insufficient. Methods: In this single-center, prospective quality improvement study, we developed a computerized mannequin simulator teaching program to assist training healthcare providers. The program included an instructive where participants were introduced to FOI through instructive videos and handouts. Participants then practiced FOI through case studies with close guidance and timely feedback from facilitators. After the session, participants evaluated the helpfulness of the curriculum for their clinical practice. Results: The program was completed by 33 participants and 30 participants completed the survey. All participants (n=30) agreed that the simulation was realistic enough to engage in learning. Approximately 90% of participants (n=27) felt that the simulation exercise helped improve technical skills and all participants (n=30) agreed that the simulation exercise was clinically applicable. Seventy-seven percent of the participants felt that they would change their practice as a result of the course. Conclusions: Our program demonstrated initial success at improving anesthesia residents’ and Certified Registered Nurse Anesthetists (CRNAs)’ confidence level with FOI knowledge and skills. Further research is needed to prove the benefits of this teaching in real patients and optimize the program.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".