Infant Robotic Cleft Palate Surgery: A Feasibility Assessment Using a Realistic Cleft Palate Simulator
Bibliographic record
Abstract
BACKGROUND: A surgical robot offers enhanced precision, visualization, and access and the potential to improve outcomes in cleft palate surgery. The goal of this study was to investigate the feasibility of using the da Vinci robot for cleft palate repair in infants using a cleft palate simulator test bed. METHODS: A high-fidelity cleft palate simulator was developed that allows performance of a robotic cleft palate repair procedure. A complete cleft palate repair was performed with the da Vinci Si with 5-mm instruments and the da Vinci Xi with 8-mm instruments. The advantages of the robotic approach were assessed in comparison with using standard instruments. For each system, arm repositioning, collisions, instrument and endoscope excursion, wrist orientation, and vision were compared for 12 steps of the repair. RESULTS: The cleft palate simulator provided a reproducible platform for testing robotic cleft palate surgery. The advantages of the robotic approach were the ability to articulate a miniature wrist intraorally with superior visualization, increased ambidexterity, and improved ergonomics compared with using standard instruments. Cleft palate repair with the Xi was superior to the Si with respect to arm repositioning, instrument collisions and excursion, and wrist orientation. However, Xi performance remained suboptimal because of the larger instruments. CONCLUSIONS: Robotic cleft palate repair using the da Vinci system offers advantages compared with the traditional approach. Cleft palate repair is more feasible with the Xi and 8-mm instruments. However, performance is limited by the instrumentation, which requires modification to ensure safety and efficacy. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, V.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".