Benign liver masses: imaging with microbubble contrast agents.
Bibliographic record
Abstract
Benign focal liver lesions are frequently encountered in routine ultrasound (US) scanning as well as in staging US examination for the patients with known malignancy. Noninvasive characterization of benign liver masses by imaging features has been a challenge for the radiologist. Some benign liver masses show typical findings on US; however, these findings are not highly specific. Contrast-enhanced ultrasound (CEUS) is useful to make an instant, confident diagnosis of benign liver masses. Contrast-enhanced multiphasic computed tomography (CT) is an excellent imaging technique to detect and characterize focal liver masses. But there are a considerable number of indeterminate focal liver lesions, which require further evaluation. CEUS provides the evaluation of perfusion and hemodynamics of nodular liver lesions as well as real-time morphologic evaluation of lesion vascularity. Most benign liver masses show characteristic features on CEUS, allowing an accurate diagnosis. This review article describes typical enhancement features of common benign liver masses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".