Guidelines from the Canadian Association of Pathologists for establishing a telepathology service for anatomic pathology using whole-slide imaging
Bibliographic record
Abstract
The use of telepathology for clinical applications in Canada has steadily become more attractive over the last 10 years, driven largely by its potential to provide rapid pathology consulting services throughout the country regardless of the location of a particular institution. Based on this trend, the president of the Canadian Association of Pathologists asked a working group consisting of pathologists, technologists, and healthcare administrators from across Canada to oversee the development of guidelines to provide Canadian pathologists with basic information on how to implement and use this technology. The guidelines were systematically developed, based on available medical literature and the clinical experience of early adopters of telepathology in Canada. While there are many different modalities and applications of telepathology, this document focuses specifically on whole-slide imaging as applied to intraoperative pathology consultation (frozen section), primary diagnosis, expert or second opinions and quality assurance activities. Applications such as hematopathology, microbiology, tumour boards, education, research and technical and/or standard-related issues are not covered.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".