Evaluation of Remote Surgical Hands-on Training in Veterinary Education Using a Hololens Mixed Reality Head-Mounted Display
Bibliographic record
Abstract
Conferencing system-assisted online classes have been conducted worldwide since the COVID-19 pandemic, and the use of three-dimensional glasses may improve pre-clinical veterinary education. However, students' satisfaction with this technique rather than their ability to perform surgery using these items has not been assessed. This study could potentially assess students' satisfaction with technique/instruction rather than their ability to perform surgery using these items.This study aimed to evaluate the effectiveness of remote online hands-on training in veterinary education using 3D glasses. Sixty students enrolled at the Faculty of Veterinary Medicineat Yamaguchi University voluntarily participated and were randomly divided into a 3D glasses and tablet group, each with 30 students. Each student completed one orthopedic and one ophthalmological task. The orthopedic task was performing surgery on a limb model, whereas the ophthalmological task involved incising a cornea on an eye model. The 3D glasses group participated in the ophthalmology task, then the orthopedic task, at a separate venue from the instructor. The tablet group participated in the same tasks using a tablet. In the student questionnaire, orthopedic screw fixation showed significantly higher levels of satisfaction in the 3D glasses group than in the tablet group, indicating a preference for this method. By contrast, for ophthalmic corneal suturing, the tablet group showed a significantly higher level of satisfaction than the 3D glasses group. Our findings showed that 3D glasses have a high educational value in practical training requiring depth and angle information.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".