Development and Evaluation of a High-Fidelity Rhinoplasty Simulator
Bibliographic record
Abstract
SUMMARY: Rhinoplasty is a challenging procedure with a steep learning curve. Surgical simulators provide a safe platform to gain hands-on experience without compromising patient outcomes. Therefore, rhinoplasty is an ideal procedure to benefit from an effective surgical simulator. A high-fidelity rhinoplasty simulator was developed using three-dimensional computer modeling, three-dimensional printing, and polymer techniques. The simulator was tested by six surgeons with experience in rhinoplasty to assess realism, anatomic accuracy, and value as a training tool. The surgeons performed common rhinoplasty techniques and were provided a Likert-type questionnaire assessing the anatomic features of the simulator. A variety of surgical techniques were performed successfully using the simulator, including open and closed approaches. Bony techniques performed included endonasal osteotomies and rasping. Submucous resection with harvest of septal cartilage, cephalic trim, and tip suturing, as well as grafting techniques including alar rim, columellar strut, spreader, and shield grafts, were performed successfully. Overall, there was agreement on the simulator's anatomic accuracy of bony and soft-tissue features. There was strong agreement on the simulator's overall realism and value as a training tool. The simulator provides a high-fidelity, comprehensive training platform to learn rhinoplasty techniques to augment real operating experience without compromising patient outcomes.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".