Evaluation of a 3D Printed Silicone Oral Cavity Cancer Model for Surgical Simulations
Bibliographic record
Abstract
Adequate surgical margins are essential in oral cancer treatment, this is, however, difficult to appreciate during training. With advances in training aids, we propose a silicone-based surgical simulator to improve training proficiency for the ablation of oral cavity cancers. A silicone-based tongue cancer model constructed via a 3D mold was compared to a porcine tongue model used as a training model. Participants of varying surgical experience were then asked to resect the tumors with clear margins, and thereafter asked to fill out a questionnaire to evaluate the face and content validity of the models as a training tool. Eleven participants from the Otolaryngology-Head and Neck Surgery unit were included in this pilot study. In comparison to the porcine model, the silicone model attained a higher face (4 vs. 3.6) and content validity (4.4 vs. 4.1). Tumor consistency was far superior in the silicone model compared to the porcine model (4.1 vs. 2.8, p = 0.0042). Fellows and staff demonstrated a better margin clearance compared to residents (median 3.5 mm vs. 1.0 mm), and unlike the resident group, there was no incidence of positive margins. The surgical simulation was overall useful for trainees to appreciate the nature of margin clearance in oral cavity cancer ablation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".