Online Histology modules for first‐year medical students: a student to student approach
Bibliographic record
Abstract
Three online training modules to introduce Histology were created by a second‐year medical student to provide a strong foundation for effective and efficient learning in the Histology laboratory. Members of all four medical school classes were surveyed for feedback on the most challenging and confusing concepts in Histology. Based on the results of over one hundred and forty student replies across all years, three online video modules were created: Approaching Histology; Introduction to Slide Preparation and Common Stains; and Introduction to Imagescope (digital slide viewing system). Content ranged from an overview of common stains and slide artifacts to understanding how to extrapolate 3‐ dimensional structures from 2‐dimensional slides. These modules are unique as they were created from the perspective of a fellow student and targeted the most common needs identified by the student‐body survey. This student‐initiated summer project was funded by the University of British Columbia Department of Cellular and Physiological Sciences. Grant Funding Source : AAA
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.017 |
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".