Role of a computer-generated three-dimensional laryngeal model in anatomy teaching for advanced learners
Bibliographic record
Abstract
OBJECTIVES: (1) To investigate the efficacy of a computer-generated three-dimensional laryngeal model for laryngeal anatomy teaching; (2) to explore the relationship between students' spatial ability and acquisition of anatomical knowledge; and (3) to assess participants' opinion of the computerised model. SUBJECTS AND METHODS: Forty junior doctors were randomised to undertake laryngeal anatomy study supplemented by either a three-dimensional computer model or two-dimensional images. Outcome measurements comprised a laryngeal anatomy test, the modified Vandenberg and Kuse mental rotation test, and an opinion survey. RESULTS: Mean scores ± standard deviations for the anatomy test were 15.7 ± 2.0 for the 'three dimensions' group and 15.5 ± 2.3 for the 'standard' group (p = 0.7222). Pearson's correlation between the rotation test scores and the scores for the spatial ability questions in the anatomy test was 0.4791 (p = 0.086, n = 29). Opinion survey answers revealed significant differences in respondents' perceptions of the clarity and 'user friendliness' of, and their preferences for, the three-dimensional model as regards anatomical study. CONCLUSION: The three-dimensional computer model was equivalent to standard two-dimensional images, for the purpose of laryngeal anatomy teaching. There was no association between students' spatial ability and functional anatomy learning. However, students preferred to use the three-dimensional model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".