The Role of Simulation in Teaching Sinus Surgery in Otolaryngology Residency: A Survey of Rhinologists
Bibliographic record
Abstract
BACKGROUND: Simulation is currently recognized as an effective surgical training tool. However, no standardized curriculum exists for endoscopic sinus surgery (ESS) simulation training. The goal of this study was to obtain an understanding of current ESS simulation use to aid the future development of an ESS training curriculum. METHODS: A 14-question survey regarding sinus simulation in residency training was developed through the education committee of the American Rhinologic Society. The survey was administered to academic American Rhinologic Society members in the United States, Canada, and Puerto Rico. The participants provided information regarding the type, amount, and effectiveness of simulation use in their residency program. RESULTS: Responses were received from 67 training programs; 45% of the programs endorsed using simulation training, although only 23.9% used ESS simulation, and all the programs used cadavers. Only 12.5% of respondent programs required ESS simulation training before operating on live patients, and trainees had an average of <6 hours of simulation training before live operations. A majority of respondents observed subjective improvement in residents' endoscope handling, dexterity, and understanding of anatomy after ESS simulation. The greatest obstacles identified were associated cost and lack of realistic simulators. CONCLUSION: A majority of responders observed improved surgical technique and knowledge in residents after simulation training. However, <25% of the survey responders used ESS simulation and cited cost and limited availability as the most common barriers. A curriculum of validated simulators has potential to improve the quality of ESS training during residency.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".