Teaching Palatoplasty Using a High-Fidelity Cleft Palate Simulator
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".