The 35th annual advances in contrast ultrasound international bubble conference, Chicago 2021: synopsis and take-home messages
Bibliographic record
Abstract
The 35th Annual Advances in Contrast Ultrasound International Bubble Conference convened in Chicago, IL, USA, on September 30th to October 1st, 2021. It featured a range of novel research from animal studies to clinical applications in multiple organ systems, demonstrating the utility of contrast enhanced ultrasound (CEUS). A multidisciplinary group of experts on the use of CEUS, including physicians, basic scientists, engineers, and industry partners, convened to discuss cutting edge research and new applications for CEUS. The conference demonstrated the wide range of CEUS uses and potential uses, including cardiac risk stratification, sonothrombolysis, peripheral vascular reperfusion, liver and renal mass evaluation, lymphatic evaluation, sentinel node identification, and CEUS use in pediatrics. The International Contrast Ultrasound Society uses this information to continue advocating for the safe and appropriate use of CEUS.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
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".