Conducting an Endoscopic Sinus Surgery Dissection Course via Telesimulation: An Initial Experience
Bibliographic record
Abstract
Objective Medical education has been severely disrupted by the COVID‐19 pandemic, with many in‐person educational activities transitioned to distance learning. To overcome this challenge, we utilized telesimulation to conduct an endoscopic sinus surgery (ESS) dissection course. Our objectives were to evaluate the effectiveness and acceptability of telesimulation as an alternative to in‐person dissection courses for resident training. Study Design Cross‐sectional study. Setting Academic medical centers. Methods The course, consisting of lectures and hands‐on dissection, was conducted entirely over the Zoom platform. The participants were allocated outpatient clinic rooms at 2 hospitals, while the instructors supervised remotely. We utilized the camera systems in the clinics and 3‐dimensional–printed sinus models for the dissection. Laptops with cameras were used to capture the endoscopic image and the dissector. We evaluated the effectiveness of telesimulation, the surgical skills of the participants, and the course by way of pre‐ and posttest and a questionnaire. Results A total of 8 participants and 7 instructors participated in the study. Telesimulation was found to be effective in helping participants gain knowledge and skills in ESS. All participants improved on their pretest scores (31.5% vs 73.4%, P =. 003) and felt more comfortable with ESS postcourse (1.9 vs 3.2, P =. 008). Participants and instructors opined that telesimulation is an acceptable alternative to in‐person dissection courses. Conclusion Telesimulation is an effective, acceptable, and viable alternative to in‐person dissection courses. It also has the advantage of overcoming temporal and geographic constraints to surgical training in 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".