A Novel Canine Otoscopy Teaching Model for Veterinary Students
Bibliographic record
Abstract
Otoscopic evaluation using an otoscope is an important tool among the diagnostic modalities for otitis externa and is considered a core component of a canine patient's complete physical examination. Traditionally, otoscopic training in veterinary school involves using live dogs (i.e., laboratory dogs or dogs that are patients of the veterinary teaching hospital). While this approach has its advantages, performing otoscopic examination on live dogs presents several challenges: it requires adequate patient restraint, can cause stress to the dog, and can potentially cause trauma and/or injury to the dog's ear canal when performed by an inexperienced individual. Using an alternative teaching tool for otoscopic evaluation could overcome these challenges and improve veterinary students' learning experience. In this study, we investigated student perceptions of a novel canine teaching model for otoscopic evaluation in first-year veterinary students. The Elnady preservation technique was employed to create a realistic, durable, and flexible model for otoscopic training in a dermatology laboratory session in a first-year veterinary course. Student feedback was assessed on a Likert scale, and overall feedback indicated that students felt that the model was beneficial for skill building and removed many of the stressors incurred with using live animals when training in clinical skills. Most students stated that they would like to have additional similar models incorporated into training and would recommend these models to other students.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".