Use of a Versatile, Inexpensive Ophthalmoscopy Teaching Model in Veterinary Medical Student Education Increases Ophthalmoscopy Proficiency
Bibliographic record
Abstract
Ophthalmoscopy is an important examination technique in the diagnosis of disease. Although it is difficult to learn, practice increases confidence and proficiency. Practicing ophthalmoscopy on live animals presents an additional level of complexity, so we sought to evaluate how students would respond to practicing ophthalmoscopy on an ocular fundus model. We constructed a simple and inexpensive model and allowed half of the students (49/100) in a first-year veterinary medicine class to practice ophthalmoscopy (direct, PanOptic, and indirect) for 20 minutes using the model. Students completed a questionnaire regarding ease of use, enjoyment, and recommendations for future use of the model immediately after the practice session. Six weeks later, we tested students’ ability to correctly match a fundus to a photograph using indirect ophthalmoscopy. All students who used the model rated it as ‘easy’ or ‘somewhat easy’ to use. All students reported that they ‘enjoyed’ (93.9%) or ‘somewhat enjoyed’ (6.1%) using the model. Also, all students who used the model stated the models should continue to be used to aid student learning. Students who used the model were significantly more likely ( p = .013) to correctly match a fundus photograph to the fundus being observed than students who had not used the model. These findings demonstrate that the model used in this study is well received by students and results in discernible gains in proficiency.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".