Use of whole slide imaging (WSI) for distance teaching
Bibliographic record
Abstract
BACKGROUND: Time, travel and financial constraints have meant that traditional visiting teaching engagements are more difficult to accomplish. This has been exacerbated with the advent of the COVID-19 pandemic. The use of digital pathology and whole slide imaging (WSI) as an educational tool for distance teaching is underutilised and not fully exploited. This paper highlights the utility and feedback on the use of WSI for distance education/teaching. MATERIALS AND METHODS: Building on an existing relationship with the University of the West Indies (UWI), pathologists at University Health Network, Toronto, provided distance education using WSI, a digitised slide image hosting repository and videoconferencing facilities to provide case-based teaching to 15 UWI pathology trainees. Feedback was obtained from residents via a questionnaire and from teachers via a discussion. RESULTS: There was uniform support from teachers who felt that teaching was not hampered by the 'virtual' engagement. Comfort levels grew with each engagement and technical issues with sound diminished with the use of a portable speaker. The residents were very supportive and enthusiastic in embracing this mode of teaching. While technical glitches marred initial sessions, the process evened out especially when the slide hosting facility, teleconferencing and sound issues were changed. CONCLUSIONS: There was unanimous endorsement that use of WSI was the future, especially for distance teaching. However, it was not meant to supplant the use of glass slides in their current routine, daily practice.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".