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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".