The Efficacy of Online Case-Based Assignments in Teaching Veterinary Ophthalmology
Bibliographic record
Abstract
Veterinarians are required to use clinical reasoning skills to successfully manage their patients with eye diseases. Case-based assignments can be an effective tool for teaching problem-solving skills. Very few models or online modules exist to deepen the instruction of veterinary ophthalmic clinical reasoning skills. The current study aims to assess the value of online case-based assignments given to students during the Ontario Veterinary College's Phase 4 ophthalmology rotation over a 4-year period. Nine case-based assignments were developed as an online module and provided signalment, history, ophthalmic database, and clinical photography. For each case, students were required to describe the ocular lesions, provide a diagnosis, and develop a short-term and long-term treatment plan. A grading rubric was created, and student feedback was collected using an online survey. A frequency analysis was conducted to evaluate final grades across each case. This analysis was also completed for grades of each question across all cases. A total of 285 students were graded individually. Students' grades were normally distributed across each assignment. Students performed better on lower-order cognitive skills (description of ocular lesions) but poorer on high-order cognitive skills (therapeutic plans). These results suggest that students tend to have difficulty with the analysis and interpretation of these cases. Student feedback reported case-based assignments were useful. Online case-based assignments may be a useful adjunctive teaching tool for students rotating through ophthalmology in their clinical year, and this tool could be considered for other specialized rotations.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".