Impacts of a large and decentralized telepathology network in Canada
Bibliographic record
Abstract
Background: Telepathology is one of the fast growing segment of the telemedicine field and Canada is recognized as a world leader in this particular domain. . Introduction: We report a benefits evaluation study of a decentralized telepathology network deployed in Eastern Quebec. The project involves 18 hospitals, making it one of the largest telepathology networks in the world. Materials and Methods: We first conducted 43 semi-structured interviews with telepathology users and managers. Hard data on the impacts of the telepathology network (e.g. the number of service disruptions, the average time between initial diagnosis and surgery) was also extracted and analyzed, where available. Results: Users found the system to be easy to use and the quality of the virtual slides and images was also considered satisfactory by pathologists. A key objective was to provide continuous coverage of intraoperative consultations in hospitals with no pathologist. Our findings show that no service disruptions were recorded in the se sites. Surgeons agreed that the use of telepathology helped avoid second surgeries and improved accessibility to care services. Telepathology was also perceived by respondents as having positive impacts on remote hospitals’ ability to retain and recruit specialists. Discussion: The observed benefits should not leave the impression that implementing telepathology is a trivial matter. Indeed, many technical, human and organizational challenges may be encountered. Conclusions: Telepathology can be highly useful in regional hospitals that do not have a pathologist on site. More research is needed to investigate the challenges and benefits associated with this growing form of telemedicine.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".