Pilot Study Evaluating the Use of Typodonts (Dental Models) for Teaching Veterinary Dentistry as Part of the Core Veterinary Curriculum
Bibliographic record
Abstract
Periodontal disease is one the most common disease pathologies in small animal medicine, yet new graduates report they feel unprepared to perform dentistry in general practice. Novel methodologies to close the knowledge gap in veterinary dentistry need to be identified. Typodonts (dental models) are commonly used in human dental schools to teach basic principles prior to practice on clinical patients and have been shown to be an effective teaching tool. The study aimed to determine if independent study and self-guided practice on a veterinary typodont prior to a structured, in-person cadaver laboratory with feedback increases students' perceived dentistry clinical skills in performing periodontal techniques. We calculated the knowledge gap before and after the cadaver laboratory by comparing the students' perceived and desired skill levels in performing periodontal charting, ultrasonic cleaning, hand scaling, and root planing. Ninety-six percent of students reported that practice with the dental typodont prior to the cadaver laboratory increased their comfort level in performing periodontal skills. However, practice did not result in a significant decrease in knowledge gap compared with participation in the cadaver laboratory alone. Although students perceived a benefit to practicing with the typodont, self-guided practice was not effective in decreasing the knowledge gap, most likely due to a lack of structured feedback with typodont use. Further investigation into the use of typodonts with direct feedback prior to structured laboratory or, alternatively, as an additional practice tool following a structured laboratory would further define if there is a benefit to typodont practice in veterinary dentistry.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".