Assessing Technical Performance and Determining the Learning Curve in Cleft Palate Surgery Using a High-Fidelity Cleft Palate Simulator
Bibliographic record
Abstract
BACKGROUND: This study assessed technical performance in cleft palate repair using a newly developed assessment tool and high-fidelity cleft palate simulator through a longitudinal simulation training exercise. METHODS: Three residents performed five and one resident performed nine consecutive endoscopically recorded cleft palate repairs using a cleft palate simulator. Two fellows in pediatric plastic surgery and two expert cleft surgeons also performed recorded simulated repairs. The Cleft Palate Objective Structured Assessment of Technical Skill (CLOSATS) and end-product scales were developed to assess performance. Two blinded cleft surgeons assessed the recordings and the final repairs using the CLOSATS, end-product scale, and a previously developed global rating scale. RESULTS: The average procedure-specific (CLOSATS), global rating, and end-product scores increased logarithmically after each successive simulation session for the residents. Reliability of the CLOSATS (average item intraclass correlation coefficient (ICC), 0.85 ± 0.093) and global ratings (average item ICC, 0.91 ± 0.02) among the raters was high. Reliability of the end-product assessments was lower (average item ICC, 0.66 ± 0.15). Standard setting linear regression using an overall cutoff score of 7 of 10 corresponded to a pass score for the CLOSATS and the global score of 44 (maximum, 60) and 23 (maximum, 30), respectively. Using logarithmic best-fit curves, 6.3 simulation sessions are required to reach the minimum standard. CONCLUSIONS: A high-fidelity cleft palate simulator has been developed that improves technical performance in cleft palate repair. The simulator and technical assessment scores can be used to determine performance before operating on patients.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".