Evaluation and Implementation of 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 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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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it