Development of and Validity Evidence for a Canine Ocular Model for Training Novice Veterinary Students to Perform a Fundic Examination
Bibliographic record
Abstract
Indirect fundoscopy is challenging for novice learners, as patients are often intolerant of the procedure, impeding development of proficiency. To address this, we developed a canine ocular simulator that we hypothesized would improve student learning compared to live dogs. Six board-certified veterinary ophthalmologists and 19 second-year veterinary students (novices) performed an indirect fundic examination on the model and live dog. Prior to assessment, novices were introduced to the skill with a standardized teaching protocol and practiced (without feedback) with either the model ( n = 10) or live dog ( n = 9) for 30 minutes. All participants evaluated realism and usefulness of the model using a Likert-type scale. Performance on the live dog and model was evaluated in all participants using time to completion of task, performance of fundic examination using a checklist and global score, identification of objects in the fundus of the model, and evaluation of time spent looking at the fundus of the model using eye tracking. Novices (trained on simulator or live dogs) were compared in fundic examination performance on the live dog and identification of shapes in the model. In general, experts performed the fundic examination faster ( p ≤ .0003) and more proficiently than the novices, although there were no differences in eye tracking behavior between groups ( p ≥ .06). No differences were detected between training on simulator versus live dog in development of fundoscopy skills in novices ( p ≥ .20). These findings suggest that this canine model may be an effective tool to train students to perform fundoscopy.
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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".