Teaching Medical Pathology in the Twenty-First Century: Virtual Microscopy Applications
Bibliographic record
Abstract
Virtual microscopy (VM) has been implemented and evaluated in the histology and general and systemic pathology courses at the University of Iowa Carver College of Medicine. Advantages of VM over traditional microscopy include accessibility and efficiency of learning and the ability to integrate VM with computer-assisted interactive learning. Advantages of using VM as opposed to digital photomicrographs include the ability to pan and zoom, explore the slide, and make independent observations. Although VM is used in a case-based format for teaching histopathology to medical students at the University of Iowa, VM may also be effectively implemented in other medical-student teaching models, including integrated and problem-based learning curricula and the classical pathology laboratory. Additional Iowa venues and courses using VM teaching include pathology of human disease for bioscience graduate students, cytology education, a comparative pathology research resource, and histology and histopathology for veterinary medicine. This article reviews the history and evolution of VM in medical pathology and its implementation at Iowa.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".