Evaluation of a three-dimensional educational computer model of the larynx: voicing a new direction.
Why this work is in the frame
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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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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