Virtual Microscopy in Histopathology Training: Changing Student Attitudes in 3 Successive Academic Years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several veterinary faculties have integrated virtual microscopy into their curricula in recent years to improve and refine their teaching techniques. The many advantages of this recent technology are described in the literature, including remote access and an equal and constant slide quality for all students. However, no study has analyzed the change of perception toward virtual microscopy at different time points of students' academic educations. In the present study, veterinary students in 3 academic years were asked for their perspectives and attitudes toward virtual microscopy and conventional light microscopy. Third-, fourth-, and fifth-year veterinary students filled out a questionnaire with 12 questions. The answers revealed that virtual microscopy was overall well accepted by students of all academic years. Most students even suggested that virtual microscopy be implemented more extensively as the modality for final histopathology examinations. Nevertheless, training in the use of light microscopy and associated skills was surprisingly well appreciated. Regardless of their academic year, most students considered these skills important and necessary, and they felt that light microscopy should not be completely replaced. The reasons for this view differed depending on academic year, as the perceived main disadvantage of virtual microscopy varied. Third-year students feared that they would not acquire sufficient light microscopy skills. Fifth-year students considered technical difficulties (i.e., insufficient transmission speed) to be the main disadvantage of this newer teaching modality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it