Step-Specific Simulation: The Utility of 3D Printing for the Fabrication of a Low-Cost, Learning Needs-Based Rhinoplasty Simulator
Bibliographic record
Abstract
BACKGROUND: Rhinoplasty is a complex procedure that requires meticulous planning and precise execution. Plastic surgeons involved in teaching residents must balance a trainee's hands-on experience while ensuring appropriate execution of difficult maneuvers. Surgical simulation, a field of growing importance with the shift towards competency-based education, may aid in trainee skill development. Through the concept of deliberate practice, the authors looked to explore the utility and economics of 3-dimensional (3D) printing technology to develop a step-specific rhinoplasty simulator. OBJECTIVES: The main objective of this study was to address rhinoplasty skills previously identified as "learning areas of weakness" and develop a low-cost, step-specific simulator to help rhinoplasty teaching. METHODS: A patient's facial bones, upper and lower lateral cartilages, and septum were segmented from a computed tomography scan and rendered in 3D format. This was 3D printed utilizing Ultimaker Polylactic filament with a polyvinyl acetate dissolvable support for bone, a mixture of Rigur 450 and Tango plus polyjet material for cartilage, and Smooth-On Dragon Skin for skin. RESULTS: A modular simulator was developed with 3 separate, interchangeable components with a perfect fit design. The simulator allowed for deliberate practice of the 5 rhinoplasty learning areas of weakness, with a maximal recurring cost of $75 CAD. CONCLUSIONS: Through the employment of 3D printing, a low-cost, maneuver-specific rhinoplasty simulator reinforcing deliberate practice was developed. This concept of simulation-based deliberate practice may be of increasing interest when considering the implementation of competency-based curricular standards in plastic surgery education.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".