Three-dimensional Educational Computer Model of the Larynx
Bibliographic record
Abstract
OBJECTIVES: To create a 3-dimensional (3D) educational computer model of the larynx, to assess the feasibility of this learning module on a Web-based platform, and to obtain student feedback on the module. DESIGN: Male and female adult cadaveric necks were scanned with microcomputed tomographic and magnetic resonance imaging scanners. Key structures were identified on each slice of the computed tomogram and/or magnetic resonance image and analyzed with a segmentation software package. Then, the images were exported into Microsoft Powerpoint. Visual text and audio commentary were added. Real cases of a child's larynx, an adult with a tracheostomy, and a patient with laryngeal carcinoma were included. The computer module was launched on a password-protected, Web-based platform. PARTICIPANTS: Fifty-eight first-year medical students (38% male; mean [SD] age, 23 [1.8] years) were invited to evaluate the module and to complete a survey. RESULTS: Most students thought that the 3D computer module was effective (60%), clear (66%), and user friendly (72%); most students (81%) thought that it was easier to understand laryngeal anatomy when they could visualize it in 3D; and most students (83%) said that they would like lectures better if they were supplemented with 3D computer modules. CONCLUSION: A 3D educational computer model of the larynx has been successfully created and warmly received by medical students.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".