Validation of a Model for Teaching Canine Fundoscopy
Bibliographic record
Abstract
A validated teaching model for canine fundoscopic examination was developed to improve Day One fundoscopy skills while at the same time reducing use of teaching dogs. This novel eye model was created from a hollow plastic ball with a cutout for the pupil, a suspended 20-diopter lens, and paint and paper simulation of relevant eye structures. This eye model was mounted on a wooden stand with canine head landmarks useful in performing fundoscopy. Veterinary educators performed fundoscopy using this model and completed a survey to establish face and content validity. Subsequently, veterinary students were randomly assigned to pre-laboratory training with or without the use of this teaching model. After completion of an ophthalmology laboratory on teaching dogs, student outcome was assessed by measuring students' ability to see a symbol inserted on the simulated retina in the model. Students also completed a survey regarding their experience with the model and the laboratory. Overall, veterinary educators agreed that this eye model was well constructed and useful in teaching good fundoscopic technique. Student performance of fundoscopy was not negatively impacted by the use of the model. This novel canine model shows promise as a teaching and assessment tool for fundoscopy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".