Teaching Palatoplasty Using a High-Fidelity Cleft Palate Simulator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Cleft palate repair is a challenging procedure for cleft surgeons to teach. A novel high-fidelity cleft palate simulator has been described for surgeon training. This study evaluates the simulator's effect on surgeon procedural confidence and palatoplasty knowledge among learners. METHODS: Plastic surgery trainees attended a palatoplasty workshop consisting of a didactic session on cleft palate anatomy and repair followed by a simulation session. Participants completed a procedural confidence questionnaire and palatoplasty knowledge test immediately before and after the workshop. RESULTS: All participants reported significantly higher procedural confidence following the workshop (p < 0.05). Those with cleft palate surgery experience had higher procedural confidence before (p < 0.001) and after (p < 0.001) the session. Palatoplasty knowledge test scores increased in 90 percent of participants. The mean baseline test score was 28 ± 10.89 percent and 43 ± 18.86 percent following the workshop. Those with prior cleft palate experience did not have higher mean baseline test scores than those with no experience (30 percent versus 28 percent; p > 0.05), but did have significantly higher scores after the workshop (61 percent versus 35 percent; p < 0.05). All trainees strongly agreed or agreed that the simulator should be integrated into training and they would use it again. CONCLUSIONS: This study demonstrates the effective use of a novel cleft palate simulator as a training tool to teach palatoplasty. Improved procedural confidence and knowledge were observed after a single session, with benefits seen among trainees both with and without previous cleft experience.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it