Telepathology Impacts and Implementation Challenges: A Scoping Review
Bibliographic record
Abstract
CONTEXT: Telepathology is a particular form of telemedicine that fundamentally alters the way pathology services are delivered. Prior reviews in this area have mostly focused on 2 themes, namely technical feasibility issues and diagnosis accuracy. OBJECTIVES: To synthesize the literature on telepathology implementation challenges and broader organizational and societal impacts and to propose a research agenda to guide future efforts in this domain. DATA SOURCES: Two complementary databases were systematically searched: MEDLINE (PubMed) and ABI/INFORM (ProQuest). Peer-reviewed articles and conference proceedings were considered. The final sample consisted of 159 papers published between 1992 and 2013. CONCLUSIONS: This review highlights the diversity of telepathology networks and the importance of considering these distinctions when interpreting research findings. Various network structures are associated with different benefits. Although the dominant rationale in single-site projects is financial, larger centralized and decentralized telepathology networks are targeting a more diverse set of benefits, including extending access to pathology to a whole region, achieving substantial economies of scale in workforce and equipment, and improving quality by standardizing care. Importantly, our synthesis reveals that the nature and scale of encountered implementation challenges also varies depending on the network structure. In smaller telepathology networks, organizational concerns are less prominent, and implementers are more focused on usability issues. As the network scope widens, organizational and legal issues gain prominence.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".