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Record W3111335130 · doi:10.1016/j.radcr.2020.11.043

Contrast-enhanced ultrasound in the diagnosis of infiltrative hepatocellular carcinoma: A report of three cases

2020· article· en· W3111335130 on OpenAlexaff
Yasovineeth Bhogadi, Ethan Brown, Stefanie Lee

Bibliographic record

VenueRadiology Case Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsJuravinski HospitalJuravinski Cancer CentreHamilton Health SciencesUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineContrast-enhanced ultrasoundContraindicationHepatocellular carcinomaRadiologyCirrhosisUltrasoundMagnetic resonance imagingPathologyInternal medicine

Abstract

fetched live from OpenAlex

Infiltrative hepatocellular carcinoma (HCC) is a challenging imaging diagnosis due to its ill-defined appearance and variable enhancement, which may be difficult to distinguish from background changes from cirrhosis. The literature on the role of contrast-enhanced ultrasound (CEUS) in the diagnosis of infiltrative HCC is currently limited. CEUS has greater sensitivity for contrast enhancement due to its temporal resolution, and can be used when there is contraindication to CT or MRI contrast. We present 3 cases where CEUS aided in the diagnosis of infiltrative HCC in patients with equivocal CT and MRI findings and/or renal failure, with significant implications for management. As current guidelines focus on the role of CEUS in characterizing defined focal liver lesions or discrete observations on precontrast US, further studies are warranted to validate the utility of CEUS in the noninvasive diagnosis of infiltrative HCC and delineate its role in algorithms for imaging workup.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.072
GPT teacher head0.272
Teacher spread0.200 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations3
Published2020
Admission routes1
Has abstractyes

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