Evaluation of a Dental Model for Training Veterinary Students
Bibliographic record
Abstract
Periodontal disease has deleterious effects on an animal's health and potentially serious implications for its welfare. Consequently, veterinarians frequently perform routine periodontal treatment in small-animal practice. One would therefore assume that small-animal dentistry would constitute a core component of a veterinary curriculum. However, most practitioners received little or no formal training in dentistry during their veterinary degrees, and the amount of instruction students currently receive is variable, often with limited opportunities to practice. At the Royal Veterinary College, a prototype dental model was developed to address the lack of practical training; it was made using ceramic tiles, silicone sealant, and grout to emulate teeth, gingiva, and calculus, respectively. A study was conducted with third-year veterinary students to compare the outcomes of learning to perform a professional dental cleaning using a model (group A) or a video (group B). Performance was assessed using an objective structured clinical examination. Students in group A scored significantly better than those in group B (p<.001). All students also completed a questionnaire evaluating attitudes toward the use of a dental model in learning dentistry-related skills. All students identified a model as a potentially valuable learning tool to supplement existing teaching methods and facilitate the acquisition of small-animal dentistry skills. The dental model has the potential to equip students with useful, practical skills in a safe and risk-free environment.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".