3D Ultrasound Measurement of Change in Carotid Plaque Volume
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: New therapies are being developed that are antiatherosclerotic but that lack intermediate end points, such as changes in plasma lipids, which can be measured to test efficacy. To study such treatments, it will be necessary to directly measure changes in atherosclerosis. The study was designed to determine sample sizes needed to detect effects of treatment using 3D ultrasound (US) measurement of carotid plaque. METHODS: In 38 patients with carotid stenosis >60%, age+/-SD 69.42+/-7.87 years, 15 female, randomly assigned in a double-blind fashion to 80 mg atorvastatin daily (n=17) versus placebo (n=21), we measured 3D plaque volume at baseline and after 3 months by disc segmentation of voxels representing carotid artery plaque, after 3D reconstruction of parallel transverse duplex US scans into volumetric 3D data sets. RESULTS: There were no significant differences in baseline risk factors. The rate of progression was 16.81+/-74.10 mm3 in patients taking placebo versus regression of -90.25+/-85.12 mm3 in patients taking atorvastatin (P<0.0001). CONCLUSIONS: 3D plaque volume measurement can show large effects of therapy on atherosclerosis in 3 months in sample sizes of approximately 20 patients per group. Sample sizes of 22 per group would be sufficient to show an effect size of 25% that of atorvastatin in 6 months. This technology promises to be very useful in evaluation of new therapies.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".