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Record W3022584729 · doi:10.14366/usg.19052

LI-RADS ancillary features on contrast-enhanced ultrasonography

2020· article· en· W3022584729 on OpenAlexaff
Christoph F. Dietrich, Yi Dong, Yuko Kono, Cosmin Caraiani, Claude B. Sirlin, Xin‐Wu Cui, An Tang

Bibliographic record

VenueULTRASONOGRAPHY · 2020
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineUltrasonographyHepatocellular carcinomaRadiologyBI-RADSContrast (vision)CategorizationRadiomicsContrast enhancementContrast-enhanced ultrasoundMagnetic resonance imagingArtificial intelligenceCancerInternal medicineComputer science

Abstract

fetched live from OpenAlex

Ultrasonography (renamed from the Journal of Korean Society of Ultrasound in Medicine in January 2014), the official English-language journal of the Korean Society of Ultrasound in Medicine (KSUM), is an international peer-reviewed academic journal dedicated to practice, research, technology, and education dealing with medical ultrasound, Aims and Scope:Ultrasonography (renamed from the Journal of Korean Society of Ultrasound in Medicine in January 2014), the official English-language journal of the Korean Society of Ultrasound in Medicine (KSUM), is an international peer-reviewed academic journal dedicated to practice, research, technology, and education dealing with medical ultrasound.It is published four times per year: January 1, April 1, July 1, and October 1. Original articles, topical reviews, pictorial essays, and notable case reports are published in Ultrasonography covering state-of-the-art content. Ultrasonography also serves as a medium for cooperation among physicians and specialists from around the world who are focusing on various ultrasound technology and disease problems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.236
Teacher spread0.192 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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