Assessing Performance in Simulated Cleft Palate Repair Using a Novel Video Recording Setup
Bibliographic record
Abstract
OBJECTIVE: To test the feasibility of implementing a high-fidelity cleft palate simulator during a workshop in Santiago, Chile, using a novel video endoscope to assess technical performance. DESIGN: Sixteen cleft surgeons from South America participated in a 2-day cleft training workshop. All 16 participants performed a simulated repair, and 13 of them performed a second simulated repair. The repairs were recorded using a low-cost video camera and a newly designed camera mouth retractor attachment. Twenty-nine videos were assessed by 3 cleft surgeons using a previously developed cleft palate objective structured assessment of technical skill (CLOSATS with embedded overall score assessment) and global rating scale. The reliability of the ratings and technical performance in relation to minimum acceptable scores and previous experience was assessed. RESULTS: The video setup provided acceptable recording quality for the purpose of assessment. Average intraclass correlation coefficient for the CLOSATS, global, and overall performance score was 0.69, 0.75, and 0.82, respectively. None of the novice surgeons passed the CLOSATS and global score for both sessions. One participant in the intermediate group, and 2 participants in the advanced group passed the CLOSATS and global score for both sessions. There were highly experienced participants who failed to pass the CLOSATS and global score for both sessions. CONCLUSIONS: The cleft palate simulator can be practically implemented with video-recording capability to assess performance in cleft palate repair. This technology may be of assistance in assessing surgical competence in 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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 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".