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Record W4388863561 · doi:10.4103/sjg.sjg_214_23

Endohepatology: The endoscopic armamentarium in the hand of the hepatologist

2023· review· en· W4388863561 on OpenAlexaff
Ahmed Alwassief, Said A. Al‐Busafi, Qasim L. Abbas, Khalid Al Shamousi, Sarto C. Paquin, Anand V. Sahai

Bibliographic record

VenueSaudi Journal of Gastroenterology · 2023
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsHôpital Saint-LucCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineHepatologyFatty liverLiver biopsyLiver diseaseInternal medicineSteatosisPortal hypertensionGastroenterologyEsophageal varicesSteatohepatitisCirrhosisDiseaseLiver function testsRadiologyBiopsy

Abstract

fetched live from OpenAlex

ABSTRACT: Recent advances in the field of hepatology include new and effective treatments for viral hepatitis. Further effort is now being directed to other disease entities, such as non-alcoholic fatty liver disease, with an increased need for assessment of liver function and histology. In fact, with the evolving nomenclature of fat-associated liver disease and the emergence of the term "metabolic-associated fatty liver disease" (MAFLD), new diagnostic challenges have emerged as patients with histologic absence of steatosis can still be classified under the umbrella of MAFLD. Currently, there is a growing number of endoscopic procedures that are pertinent to patients with liver disease. Indeed, interventional radiologists mostly perform interventional procedures such as percutaneous and intravascular procedures, whereas endoscopists focus on screening for and treatment of esophageal and gastric varices. EUS has proven to be of value in many areas within the realm of hepatology, including liver biopsy, assessment of liver fibrosis, measurement of portal pressure, managing variceal bleeding, and EUS-guided paracentesis. In this review article, we will address the endoscopic applications that are used to manage patients with chronic liver disease.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.665
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.067
GPT teacher head0.349
Teacher spread0.282 · 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 designObservational
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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