Virtual Microscopy Is More Effective Than Conventional Microscopy for Teaching Cytology to Veterinary Students: A Randomized Controlled Trial
Bibliographic record
Abstract
Virtual microscopy (VM) using scanned slides and imaging software is increasingly used in medical curricula alongside instruction in conventional microscopy (CM). Limited reports suggest that VM is useful in the veterinary education setting, and generally well-received by students. Whether students can apply knowledge gained through VM to practical use is unknown. Our objective was to determine whether instruction using VM, compared to CM, is a successful method of training veterinary students for the application of cytology in practice (i.e., using light microscopes). Seventy-one veterinary students from Colorado State University who attended a voluntary 3-hour cytology workshop were randomized to receive the same instruction with either VM ( n = 35) or CM ( n = 36). We compared these students to a control group ( n = 22) of students who did not attend a workshop. All students took a post-workshop assessment involving the interpretation of four cases on glass slides with CM, designed to simulate the use of cytology in general practice. Students also took an 18-question survey related to the effectiveness of the workshop, providing their opinions on cytology instruction in the curriculum and their learning preference (VM or CM). The mean assessment score of the VM group (14.18 points) was significantly higher than the control group (11.33 points, p = .003), whereas the mean of the CM group (12.77 points) was not statistically significantly different from controls ( p = .170). Not only is VM an effective method of teaching cytology to veterinary students that can be translated to a real-world case scenario, but it outperformed CM instruction in this study.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".