Microbubble-enhanced US in Body Imaging: What Role?<sup/>
Bibliographic record
Abstract
Contrast agents for ultrasonography (US) comprise microscopic bubbles of gas in an encapsulating shell. They are unique in that they interact with the imaging process, oscillating in response to a low-intensity ultrasound field and disrupting in response to a high-intensity field. New contrast-specific imaging modes allow US to show exquisite vascularity and tissue perfusion in real time and with excellent spatial resolution. In Europe, Asia, and Canada, to name only the most obvious, characterization of focal liver masses is the first and best established use of contrast-enhanced (CE) US, allowing for the noninvasive diagnosis of commonly encountered liver masses with comparable accuracy to that of computed tomography and magnetic resonance studies. CE US is a preferred modality for the difficult task of diagnosis of liver nodules detected on surveillance scans in those at risk for hepatocellular carcinoma. Newer body applications include the guidance of ablative intervention, monitoring activity of bowel inflammation in Crohn disease, characterization of kidney masses, especially cystic renal cell carcinoma, diagnosis of prostate cancer, and monitoring the response of tumors to antivascular drug therapies. Microbubble contrast agents are easy to use and robust; their use poses no risk of nephrotoxicity and requires no ionizing radiation. CE US plays a vital and expanding role that improves management and patient care.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".