Development and validation of a 3D‐printed model of the ostiomeatal complex and frontal sinus for endoscopic sinus surgery training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Endoscopic sinus surgery poses unique training challenges due to complex and variable anatomy, and the risk of major complications. We sought to create and provide validity evidence for a novel 3D-printed simulator of the nose and paranasal sinuses. METHODS: Sinonasal computed tomography (CT) images of a patient were imported into 3D visualization software. Segmentation of bony and soft tissue structures was then performed. The model was printed using simulated bone and soft tissue materials. Rhinologists and otolaryngology residents completed 6 prespecified tasks including maxillary antrostomy and frontal recess dissection on the simulator. Participants evaluated the model using survey ratings based on a 5-point Likert scale. The average time to complete each task was calculated. Descriptive analysis was used to evaluate ratings, and thematic analysis was done for qualitative questions. RESULTS: A total of 20 participants (10 rhinologists and 10 otolaryngology residents) tested the model and answered the survey. Overall the participants felt that the simulator would be useful as a training/educational tool (4.6/5), and that it should be integrated as part of the rhinology training curriculum (4.5/5). The following responses were obtained: visual appearance 4.25/5; realism of materials 3.8/5; and surgical experience 3.9/5. The average time to complete each task was lower for the rhinologist group than for the residents. CONCLUSION: We describe the development and validation of a novel 3D-printed model for the training of endoscopic sinus surgery skills. Although participants found the simulator to be a useful training and educational tool, further model development could improve the outcome.
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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 it