Evaluation of a three-dimensional educational computer model of the larynx: voicing a new direction.
Bibliographic record
Abstract
OBJECTIVE: To evaluate a novel method of teaching laryngeal anatomy. DESIGN: Prospective, randomized, controlled trial. SETTING: University educational program. METHODS: Computer model development: A three-dimensional (3D) educational computer model of the larynx was created from high-resolution computed tomography and magnetic resonance images of cadaveric necks using segmentation software (Amira) (Visage Imaging, Inc., Carlsbad, CA). E-learning authoring software (Articulate, Articulate Global, Inc, New York, NY) then was used to make the model interactive and multimedia. The model was launched on a Web-based platform. Model evaluation: One hundred students (age 23.8 +/- 2.2 years; 55% male) were randomized to either the 3D computer model group (3D group) (n = 50) or the standard written instruction group (SWI group) (n = 50). MAIN OUTCOME MEASURES: The primary outcome measure was the score on a 20-question laryngeal anatomy test; the secondary outcome measure was a student opinion questionnaire. RESULTS: The mean score on the laryngeal anatomy test was 14.2 +/- 2.8 (72.0 +/- 15.1%). The mean score for the 3D group was 13.6 +/- 3.0 (67.0 +/- 16.1%) versus 14.8 +/- 2.5 (76.0 +/- 12.7%) for the SWI group (t = 2.194, df = 98, p < .031). A majority of students felt that the 3D model was effective, clear, user-friendly, and a preferred supplement to traditional methods of instruction. The 3D group rated the computer model more enjoyable than the SWI group. CONCLUSIONS: A 3D educational computer model of the larynx was not shown to be superior to written lecture notes in its efficacy in teaching anatomy; however, it was judged to be a preferred and valuable supplement to traditional teaching methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".