Robot-Assisted Nasal Reconstruction: A Cadaveric Study
Bibliographic record
Abstract
OBJECTIVE: Manual contouring of cartilage for nasal reconstruction is tedious and time-consuming. The use of a robot could improve the speed and precision of the contouring process. This cadaveric study evaluates the efficiency and accuracy of a robot methodology for contouring the lower lateral cartilage of the nasal tip. METHODS: An augmented robot with a spherical burring tool attached was utilized to carve 11 cadaveric rib cartilage specimens. In phase 1, the right lower lateral cartilage was harvested from a cadaveric specimen and used to define a carving path for each rib specimen. In phase 2, the cartilage remained in situ during the scanning and 3-dimensional modeling. The final carved specimens were compared with the preoperative plans through topographical accuracy analysis. The contouring times of the specimens were compared with 14 retrospectively reviewed cases (2017-2020) by an experienced surgeon. RESULTS: Phase 1 root mean square error of 0.40±0.15 mm and mean absolute deviation of 0.33±0.13 mm. Phase 2 root mean square error of 0.43 mm and mean absolute deviation of 0.28 mm. The average carving time for the robot specimens was 14±3 minutes and 16 minutes for Phase 1 and Phase 2, respectively. The average manual carving by an experienced surgeon was 22±4 minutes. CONCLUSIONS: Robot-assisted nasal reconstruction is very precise and more efficient than manual contouring. This technique represents an exciting and innovative alternative for complex nasal reconstruction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".