Economy of Hand Motion During Cleft Palate Surgery Using a High-Fidelity Cleft Palate Simulator
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to assess economy of hand motion of residents, fellows, and staff surgeons using a high-fidelity cleft palate simulator to (1) stratify performance for the purpose of simulator validation and (2) to estimate the learning curve. DESIGN: Two residents, 2 fellows, and 2 staff surgeons performed cleft palate surgery on a high-fidelity cleft palate simulator while their hand motion was tracked using an electromagnetic hand sensor. The time, number of hand movements, and path length of their hands were determined for 10 steps of the procedure. The magnitude of these metrics was compared among the 3 groups of participants and utilized to estimate the learning curve using curve-fitting analysis. RESULTS: The residents required the most time, number of hand movements, and path length to complete the procedure. Although the number of hand movements was closely matched between the fellows and staff, the overall total path length was shorter for the staff. Inverse curves were fit to the data to represent the learning curve and 25 and 113 simulation sessions are required to reach within 5% and 1% of the expert level, respectively. CONCLUSION: The simulator successfully stratified performance using economy of hand motion. Path length is better matched to previous level of experience compared to time or number of hand movements.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".