Endohepatology: The endoscopic armamentarium in the hand of the hepatologist
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".