The Challenges of Implementing a “Patient-Oriented” Telepathology Network; the Eastern Québec Telepathology Project Experience
Bibliographic record
Abstract
Background: The aim of the Eastern Québec telepathology project is to provide uniform diagnostic telepathology services across a huge geographic region with a low population density. This project is intended to provide surgeons and pathologists with frozen section and second opinion services anywhere and at any time across the entire region in order to avoid unnecessary patient transfer. Methods: The project has been implemented in 21 sites, each equipped with a whole slide scanner, a macroscopy station, two videoconferencing devices and a viewer/case management and collaboration solution. Of the 21 sites, 6 are devoid of a pathology laboratory. Of the remaining 15 sites, two have no pathologists, 6 have one and 7 have two or more. Results: The project has been successful and most appreciated by pathologists and surgeons. We report a number of challenges related to change management that we had to take into account in the course of implementation of this network. The challenges underscore the need for regular visits and active support to participating centers by the project team. Conclusion: The Eastern Québec telepathology network is successful and improves medical care in this region. In the course of implementation, we encountered a number of challenges which required innovative solutions.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".