Impacts of a Large Decentralized Telepathology Network in Canada
Bibliographic record
Abstract
BACKGROUND: Telepathology is a fast growing segment of the telemedicine field. As of yet, no prior research has investigated the impacts of large decentralized telepathology projects on patients, clinicians, and healthcare systems. This study aims to fill this gap. We report a benefits evaluation study of a large decentralized telepathology project deployed in Eastern Quebec, Canada whose main objective is to provide continuous coverage of intraoperative consultations in remote hospitals without pathologists on-site. The project involves 18 hospitals, making it one of the largest telepathology networks in the world. MATERIALS AND METHODS: We conducted 43 semistructured interviews with several telepathology users and hospital managers. Archival data on the impacts of the telepathology project (e.g., number of service disruptions, average time between initial diagnosis and surgery) were also extracted and analyzed. RESULTS: Our findings show that no service disruptions were recorded in hospitals without pathologists following the deployment of telepathology. Surgeons noted that the use of intraoperative consultations enabled by telepathology helped avoid second surgeries and improved accessibility to care services. Telepathology was also perceived by our respondents as having positive impacts on the remote hospitals' ability to retain and recruit surgeons. CONCLUSIONS: 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. 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 large decentralized telepathology networks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".