Evaluation and Implementation of a High-Fidelity Cleft Palate Simulator
Bibliographic record
Abstract
BACKGROUND: Cleft palate repair is a challenging procedure to learn because of the delicate tissue handling required and the small confines of the infant oral cavity. As a result, cleft palate simulators have previously been described to augment cleft palate repair training. Although valuable, they lack the fidelity for this complex procedure. METHODS: A high-fidelity cleft palate simulator was evaluated by staff and fellows in pediatric plastic surgery who provided feedback on its realism, anatomical accuracy, and effectiveness as a training tool. The simulator was implemented within a training workshop following a didactic session on cleft palate repair and anatomy. A test was administered to each participant before and immediately after the workshop to assess knowledge transfer. Perceived confidence of performing a repair following the workshop was also assessed, as was the workshop's effectiveness. RESULTS: Overall, participants agreed that the simulator is anatomically accurate and realistic and strongly agreed that the simulator is a valuable training tool. The average test score increased from 25 percent before the workshop to 77.27 percent after the workshop. Overall, participants of the workshop felt more confident performing a repair and strongly agreed that the workshop was valuable and effective. CONCLUSIONS: A high-fidelity cleft palate simulator has been evaluated as realistic, anatomically accurate, and valuable as a training tool. The simulator was successfully integrated into a training workshop, which resulted in significant knowledge increase on anatomy and the procedure and perceived confidence and comfort in performing a cleft palate repair.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".