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Record W2927022403 · doi:10.1002/hep4.1340

Telemedicine: An Evolving Field in Hepatology

2019· article· en· W2927022403 on OpenAlexaboutno aff
Cindy Piao, Norah A. Terrault, Souvik Sarkar

Bibliographic record

VenueHepatology Communications · 2019
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
FundersAllerganGilead SciencesBristol-Myers Squibb
KeywordsTelemedicineReimbursementMedicineHealth careHepatologyModalitiesMedical emergencyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Healthcare delivery has been dramatically changing in recent times with advances in technology. One area of expansion has been the use of telemedicine due to progression in communication technologies. Telemedicine offers the opportunity to overcome barriers of access, improve patient satisfaction, improve healthcare outcomes and streamline communication between patients and providers. The primary modalities of telemedicine can be grouped into categories of 'remote monitoring,' 'store and forward' and 'interactive telemedicine.' These modalities of telemedicine have been practiced and explored within the scope of hepatology such as in liver transplantation, hepatocellular carcinoma and management of chronic hepatitis C (CHC). There are numerous telemedicine-based CHC management studies and programs that have developed in New Mexico, the Department of Veterans Affairs, as well as globally in Australia and Canada. In Northern California, the University of New Mexico telemedicine-based model of 'ECHO' has been extended to develop community-based champions to screen-link-treat CHC patients with the goal to eliminate hepatitis C. Despite the advantages to telemedicine, there are still many barriers to seamless integration due to reimbursement and up-front cost. Nevertheless, it remains an essential part in providing world-class care to liver patients across geographic and economic barriers.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.335
Teacher spread0.309 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2019
Admission routes1
Has abstractyes

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