Bibliographic record
Abstract
Recent developments in 4D ultrasound imaging technology allow clinicians to obtain not only rich visual information but also quantitative data that can be used for diagnosis and treatment. Some argue that the extension of 2D ultrasound is unnecessary and does not offer any benefits to diagnosis, while others argue that it is possible to better assess an abnormality in 3D than 2D. Anatomy can be reconstructed in perspectives that were never seen with conventional 2D US imaging. Advanced rendering techniques in three dimensions can be customized to be sensitive to specific pathology, thus making diagnosis more accurate. Volume and function of certain anatomical components can be measured with greater accuracy. This article reviews physical principles behind the ultrasound technology, how they are applied to advance the field of ultrasound imaging, and maybe reach its limits. Advances in ultrasound technology make 4D ultrasound imaging faster and less dependent on the operator's expertise, thus opening up more research possibilities in the fields of data processing and visualization. Currently, 4D ultrasound is extensively used in the field of obstetrics and interven-tional radiology. The goal of 4D ultrasound is to overcome the limitations posed by its predecessor technology and to be more clinically useful as an imaging tool.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".