History of contrast enhanced ultrasound (CEUS)
Bibliographic record
Abstract
The 50th year of the European Federation of Societies in Ultrasound in Medicine and Biology (EFSUMB) has been celebrated 2022 publishing articles on the history of US. Contrast enhanced ultrasound (CEUS) allows to visualize blood flow and tissue perfusion. CEUS has proven to be safe without risk of nephrotoxicity. The availability of a contrast agent (tracer) for ultrasound imaging allows for the first time a dynamic assessment of tissue perfusion (blood flow and wash-in/wash-out pattern) which is an essential part for the detection and characterisation of pathological tissue and abnormal organ function. It was an outstanding achievement of academic centers in close cooperation with EFSUMB to investigate and validate the clinical potential of this new technology for the diagnosis and monitoring of various diseases and to develop clinical guidelines based on an in-depth assessment of the existing scientific publications. An important part of the implementation of CEUS in clinical practice was the development of contrast-specific imaging modes on the ultrasound scanners (in close cooperation with the machine manufacturers), the optimization of the machine setups for contrast imaging and the education provided to clinical users in form of workshops, webinars, textbooks and scientific congresses.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".