Development and validation of a septoplasty training model using 3‐dimensional printing technology
Bibliographic record
Abstract
BACKGROUND: Providing alternative training modalities may improve trainees' ability to perform septoplasty. Three-dimensional printing has been shown to be a powerful tool in surgical training. The objectives of this study were to explain the development of our 3-dimensional (3D) printed septoplasty training model, to assess its face and content validity, and to present evidence supporting its ability to distinguish between levels of surgical proficiency. METHODS: Imaging data of a patient with a nasal septal deviation was selected for printing. Printing materials reproducing the mechanical properties of human tissues were selected based on literature review and prototype testing. Eight expert rhinologists, 6 senior residents, and 6 junior residents performed endoscopic septoplasties on the model and completed a postsimulation survey. Performance metrics in quality (final product analysis), efficiency (time), and safety (eg, perforation length, nares damage) were recorded and analyzed in a study-blind manner. RESULTS: The model was judged to be anatomically correct and the steps performed realistic, with scores of 4.05 ± 0.82 and 4.2 ± 1, respectively, on a 5-point Likert scale. Ninety-two percent of residents desired the simulator to be integrated into their teaching curriculum. There was a significant difference (p < 0.05) between the expert, intermediate, and novice groups in time taken and nares cuts, whereas other performance metrics showed no significant difference. CONCLUSION: To our knowledge, there are no other simulator training models for septoplasty. Our model incorporates 2 different materials mixed into the 3 relevant consistencies necessary to simulate septoplasty. Our findings provide evidence supporting the validity of the model.
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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) | 0.000 | 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".