Histoscope: A Web-Based Microscopy Tool for Oral Histology Education
Bibliographic record
Abstract
OBJECTIVES: Histology, the study of tissue structure under a microscope, is one of the most essential yet least engaging topics for health professional students. Understanding tissue microanatomy is crucial for students to be able to recognize cellular structures and follow disease pathogenesis. Traditional histology teaching labs rely on light microscopes and a limited array of slides, which inhibits simultaneous observation by multiple learners, and prevents in-class discussions. We have developed an interactive web-based microscopy tool called "Histoscope" for oral histology in this context. METHODS: Good quality microscope slides were selected for digital scanning. The slides were scanned with multiple layers of z-stacking, a method of taking multiple images at different focal distances. The digital images were checked for quality and were archived on Histoscope. The slides were annotated, and self-assessment questions were prepared for the website. Interactive components were programmed on the website to mimic the experience of using a real light microscope. RESULTS: This web-based tool allows users to interact with histology slides, replicating the experience of observing and manipulating a slide under a real microscope. Through this website, learners can access a broad array of digital oral histology slides and self-assessment questions. CONCLUSIONS: Incorporation of Histoscope in a course can shift traditional teacher-centered histology learning to a collaborative and student-centered learning environment. This platform can also provide students the flexibility to study histology at their own pace.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.020 |
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".