How to Perform Cardiac Contrast-Enhanced Ultrasound (cCEUS): Part I
Bibliographic record
Abstract
Ultrasound enhancing agents (UEAs, formerly called contrast agents) for assessments of the left heart have improved the applicability of echocardiography and the accuracy of echocardiographic measurements. UEAs have been recommended for several diagnostic echocardiographic procedures by national and supernational agencies. The increased use of UEAs during the last years provided more evidence and experience in clinical practice data which is helpful for optimizing the UEA procedures and which will be useful for both newcomers to UEA in echocardiography and sonographers/physicians with experience in echocardiography with UEAs. In two parts, this review focuses on the "how to do" for the approved UEA applications. This is part 1, covering the available UEAs and providing specific guidance on the assessment of global and regional LV function. Part 2 covers the imaging of myocardial disease and masses as well as myocardial perfusion. Recommendations include the application of UEAs in two-dimensional echocardiography as there is limited data on three-dimensional echocardiography. A step-by-step approach is proposed for each of the procedures as well as guidance on how to interpret recordings and how to report them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".