The effect of low‐fidelity endoscopic sinus surgery simulators on surgical skill
Bibliographic record
Abstract
BACKGROUND: Surgical training models are being increasingly used to provide an environment for surgical trainees to improve their skills without risk to patients. This study uses previously validated, inexpensive, low-fidelity training models to determine how pretraining affects endoscopic sinus surgery (ESS) skills. METHODS: Fourteen Otolaryngology residents were randomized to 1 of 2 groups that were stratified for training level. The first group took part in a pretraining session where they practiced on all 5 different modules whereas the second group did not receive any pretraining. The following day, all participants took part in a cadaveric ESS course. Participants were instructed to complete a set of tasks and their performances were videotaped. The videos were then evaluated using a Global Rating Scale (GRS) and a Task-Specific Checklist (TSC). The performances of those who trained using the models were compared to the performances of those who did not. RESULTS: The intervention (pretraining) group performed better than the nonintervention (no pretraining) group on the cadaveric ESS tasks (p < 0.05). As well, there was a statistical difference between the senior residents who had the pretraining with the simulator models performing better than those who did not. CONCLUSION: The modules appear to have made a positive impact on ESS skills. These low-cost, easily-constructed training modules have the potential to be integrated into Otolaryngology-Head and Neck Surgery resident training. Assessment of long-term training effects with a larger number of participants is planned for future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".