<scp>Robot‐Automated</scp> Cartilage Contouring for Complex Ear Reconstruction: A Cadaveric Study
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Auricular reconstruction requiring manual contouring of costal cartilage is complex and time consuming, which could be facilitated by a robot in a fast and precise manner. This feasibility study evaluates the accuracy and speed of robotic contouring of cadaver costal cartilage. METHODS: An augmented robot with a spherical burr was used on cadaveric rib cartilage. Using a laser scanner, each rib section was converted to a three-dimensional model for preoperative planning. A model ear was also scanned to define a carving path for each piece of cartilage. After being contoured, each specimen was compared against the preoperative plan utilizing deviation maps to analyze topographic accuracy. Contouring times of the robot were compared with 13 retrospectively reviewed cases (2006-2017) by an experienced surgeon. RESULTS: Scanning the cartilage sections took 24.8 ± 6.8 seconds. Preoperative processing took an additional 29.9 ± 8.9 seconds for the preparation of the contouring path. Once the path was prepared, the robot contoured the specimens with a root mean square error of 0.54 mm and a mean absolute deviation of 0.40 mm. The average time to contour the specimens with the robot was 13 ± 2 minutes compared to 71 ± 6 minutes for the manual contouring in the reviewed cases. CONCLUSIONS: The accuracy of the robotic system was high, with submillimeter deviations from the preoperative plan. The robot required <20% of the contouring time compared to the experienced surgeon. This represents a fast and accurate alternative to hand-contouring costal cartilage grafts for auricular reconstruction. Laryngoscope, 131:1002-1007, 2021.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".