A High Fidelity Cleft Lip Simulator
Bibliographic record
Abstract
BACKGROUND: Cleft lip surgery is technically difficult requiring precise planning and understanding of 3-dimensional structures to obtain an optimal outcome. A physical cleft lip simulator was developed that allows trainees to gain experience in cleft lip repair and primary rhinoplasty before operating on real patients. METHODS: A cleft lip simulator that comprises multilayered soft tissues, bone, and realistic dissection planes was developed using 3D printing, adhesive and polymer techniques. Four experienced cleft surgeons performed a total of 7 simulated repairs on the simulator. Feedback on the realism and value of the simulator was obtained from the surgeons. RESULTS: Six of the repairs were a Fisher anatomic subunit approximation technique, and 1 was a rotation advancement repair. All repairs were completed with successful performance of markings, incisions, dissections, and multilayered closure. All surgeons agreed that the simulator is realistic and that the simulator is a valuable tool for training in cleft lip surgery. CONCLUSIONS: A cleft lip simulator that allows performance of a cleft lip repair and primary rhinoplasty from start to finish was developed and pilot tested. The simulator provides a training platform to gain experience in cleft lip repair before operating on real 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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".