Blood Flow Patterns in Focal Liver Lesions at Microbubble-enhanced US
Bibliographic record
Abstract
Noninvasive diagnosis of liver lesions is usually performed with contrast material-enhanced computed tomography (CT) and magnetic resonance (MR) imaging and is based on enhancement features of the arterial and portal venous phases. Ultrasonography (US) is often limited in characterizing liver lesions because color and spectral Doppler US provide limited vascular information in large patients and in small or deep lesions. However, microbubble contrast agents, together with specialized US techniques, now allow diagnosis of liver lesions based on morphologic evaluation of lesion vascularity and visualization of specific enhancement features. Microbubble contrast agents are purely intravascular, easy to administer, and well tolerated and allow sensitive real-time evaluation of blood flow in hepatic lesions. During the portal venous phase, benign lesions (eg, hemangioma, focal nodular hyperplasia) typically enhance more than the liver, whereas malignant lesions (eg, hepatocellular carcinoma, metastases) enhance less. Microbubble-enhanced US allows characterization of very small lesions that may not be accurately characterized with CT or MR imaging. Findings from initial studies suggest that microbubble-enhanced US of the liver provides enhancement information comparable to that provided by contrast-enhanced CT and MR imaging, along with real-time morphologic evaluation of lesion vascularity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".