Comparison of Three Canine Models for Teaching Veterinary Dental Cleaning
Bibliographic record
Abstract
Veterinary dental cleaning prevents and treats periodontal disease, one of the most common diagnoses in small animal practice. Students learn to perform dental cleaning through deliberate practice, which can be gained through working on models. This study compared educational outcomes after students ( n = 36) were randomized to practice on one of three dental cleaning models: a low-fidelity ceramic tile, a mid-fidelity three-dimensional (3D) printed canine skull model, or a high-fidelity canine head model. Students provided survey feedback about their model and later performed a dental cleaning on a canine cadaver head while being video-recorded. Experts ( n = 10) provided feedback on each model. Experts agreed that all models were suitable for teaching dental cleaning, but the 3D skull and full head models were more suitable for assessing student skill ( p = .002). Students were also more positive about the realism and features of those two models compared to the tile model. Students practicing on each of the models were equally effective at removing calculus from the cadavers’ teeth. Students who learned on the tile model were a median of 4 minutes slower to remove calculus from their cadaver’s teeth than students who trained on the canine head model. Although students may be more accepting of the 3D skull and full head models, all three models were equally effective at teaching the skill. Experts approved all models for teaching, but recommended the 3D skull or full head model if student skills were to be assessed. Low-fidelity models remain effective training tools with comparable learning outcomes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".