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Record W2050452150 · doi:10.4103/2153-3539.153906

2014 American Telemedicine Association clinical guidelines for telepathology: Another important step in support of increased adoption of telepathology for patient care

2015· article· en· W2050452150 on OpenAlexaffabout
Andrew Evans, Elizabeth A. Krupinski, Ronald S. Weinstein, Liron Pantanowitz

Bibliographic record

VenueJournal of Pathology Informatics · 2015
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsTelepathologyTelemedicineMedical diagnosisVirtual microscopyMedicineSecond opinionDigital pathologyStandardizationMedical physicsModalitiesPathologyComputer scienceHealth care

Abstract

fetched live from OpenAlex

Definition of key termsYes (with comprehensive glossary) Yes (basic terminology only 0) Yes (basic terminology only) Telepathology modalities All types All types Whole slide imaging only List of telepathology applications Yes Yes Yes Details on technical specifications Yes

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.122
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0040.004
Scholarly communication0.0090.007
Open science0.0050.006
Research integrity0.0150.021
Insufficient payload (model declined to judge)0.0280.017

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 head0.179
GPT teacher head0.494
Teacher spread0.316 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations41
Published2015
Admission routes2
Has abstractyes

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