Contrast-enhanced ultrasound in the diagnosis of nodules in liver cirrhosis
Bibliographic record
Abstract
Contrast-enhanced ultrasound (CEUS) using microbubble contrast agents are useful for the diagnosis of the nodules in liver cirrhosis. CEUS can be used as a problem-solving method for indeterminate nodules on computed tomography (CT) or magnetic resonance imaging (MRI) or as an initial diagnostic test for small newly detected liver nodules. CEUS has unique advantages over CT and MRI including no renal excretion of contrast, real-time imaging capability, and purely intravascular contrast. Hepatocellular carcinoma (HCC) is characterized by arterial-phase hypervascularity and later washout (negative enhancement). Benign nodules such as regenerative nodules or dysplastic nodules are usually isoechoic or slightly hypoechoic in the arterial phase and isoechoic in the late phase. However, there are occasional HCC lesions with atypical enhancement including hypovascular HCC and hypervascular HCC without washout. Cholangiocarcinomas are infrequently detected during HCC surveillance and mostly show rim-like or diffuse hypervascularity followed by rapid washout. Hemangiomas are often found at HCC surveillance and are easily diagnosed by CEUS. CEUS can be effectively used in the diagnostic work-up of small nodules detected at HCC surveillance. CEUS is also useful to differentiate malignant and benign venous thrombosis and to guide and monitor the local ablation therapy for HCC.
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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 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".