Clinical Utility of Three-dimensional US
Bibliographic record
Abstract
Three-dimensional (3D) ultrasonography (US) is rapidly gaining popularity as it moves out of the research environment and into the clinical setting. This modality offers several distinct advantages over conventional US, including 3D image reconstruction with a single pass of the US beam, virtually unlimited viewing perspectives; accurate assessment of long-term effects of treatment; and more accurate, repeatable evaluation of anatomic structures and disease entities. In obstetric imaging, 3D US provides a novel perspective on the fetal anatomy, makes anomalies easier to recognize, facilitates maternal-fetal bonding, and helps families better understand fetal abnormalities. Three-dimensional pelvic US allows volume data sets to be acquired with both transvaginal and transabdominal probes. Viewing multiple 3D power Doppler US images in a fast cine loop has proved useful in angiographic applications. Three-dimensional prostate US can help make accurate volume assessments for dosimetry planning or for estimating prostate-specific antigen levels. In breast imaging, 3D US has the capacity to demonstrate lesion margins and topography, thereby helping differentiate benign from malignant masses. Three-dimensional US can also help determine the need for biopsy and help facilitate needle localization and guidance during biopsy. With recent advances in computer technology and display techniques, 3D US will likely play an increasingly important role in medicine.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".