Introduction and Evaluation of Virtual Microscopy in Teaching Veterinary Cytopathology
Bibliographic record
Abstract
Virtual microscopy (VM) uses a computer to view digitized slides and is comparable to using a microscope to view glass slides. This technology has been assessed in human medical education for teaching histology and histopathology, but, to the authors' knowledge, no one has evaluated its use in teaching cytopathology in veterinary medical education. We hypothesize that students will respond positively to the use of VM for viewing cytopathology preparations and that the technology can be successfully used for student assessment. To test this hypothesis, we surveyed students regarding their level of satisfaction with features of the VM system, their preference for use of VM in the curriculum, and the potential influence virtual slides may have on student study habits; student performance on a traditional cytopathology practical examination and a similar exam using VM was evaluated. Our results show that student perception of the VM system is generally very positive, with some concerns about resolution and the need for continued exposure to traditional microscopy. Within the curriculum, students indicated a preference for the option of using virtual slides for studying and take-home exercises. Overwhelmingly, students wanted either hybrid laboratory sessions or sessions using glass slides with virtual slides available for study and review. Students identified many VM test-taking features as advantageous compared with traditional glass-slide practical exams as traditionally administered. However, students indicated a strong preference for continued use of traditional microscopy for graded practical exams. Students may be more likely to study slides in preparation for practical examinations if virtual slides are available. Results also indicate that VM can be used successfully for assessment purposes, but students should receive training in using virtual slides if the technology will be used for assessment.
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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 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".