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Record W2336972508 · doi:10.1159/000442390

International Telepathology: Promises and Pitfalls

2016· review· en· W2336972508 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePathobiology · 2016
Typereview
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsTelepathologyWorkflowAccreditationTelemedicineBusinessHealth careProcess managementEngineering managementKnowledge managementComputer scienceMedicineEngineeringMedical educationPolitical science

Abstract

fetched live from OpenAlex

Innovative technologies for digital imaging and telecommunications are changing the way we deliver health care. Telepathology collaborations are one example of how delivering remote pathology services to patients can benefit from leveraging this change. Over the years, several academic and commercial teleconsultation networks have been established. Herein, we review the landscape of these international telepathology efforts and highlight key supportive factors and potential barriers to successful cross-border collaborations. Important features of successful international telepathology programs include efficient workflows, dedicated information technology staff, continuous maintenance, financial incentives, ensuring that all involved stakeholders are satisfied, and value-added clinical benefit to patient care. Factors that plague such telepathology operations include legal/regulatory issues, sustainability, and cultural and environmental issues. Pathologists, vendors and laboratory accreditation agencies will need to embrace and capitalize on this new paradigm of international telepathology accordingly.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.331
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it