Prospective cardiac gating of carotid three‐dimensional ultrasound
Bibliographic record
Abstract
Quantitative measurements of carotid atherosclerosis can be determined using three-dimensional ultrasound (3DUS). This pilot study involved the development of prospective cardiac gating of 3DUS carotid images to reduce cardiac cycle-derived arterial pulsatility. The method developed uses electrocardiograph signal detection of the cardiac cycle R wave with imaging acquisition delayed in time (deltat) after the R wave is detected. Pulsatility of the common carotid artery was measured by calculating the mean percentage change in arterial cross-sectional area (%deltaA) in moderate atherosclerosis (MA) patients (12% +/- 1%) and healthy volunteers (HVs) (16% +/- 3%) and found that %deltaA was significantly higher for HV than for MA (p = 0.016) when no cardiac gating was used. The cardiac gating method was tested with deltat = 250 ms and deltat = 400 ms in young healthy volunteers and rheumatoid arthritis (RA) patients. For all 3DUS measurements acquired without gating, there was a significant association between %deltaA and age (r2 = 0.20, p = 0.035), and mean %deltaA (in HV and RA) was 13% +/- 5% [95% confidence interval (CI) = 10%-17%]. For deltat = 250 ms mean %deltaA was significantly different and decreased to 7% +/- 3% (95% CI = 5%-10%) and for deltat = 400 ms it was significantly different and decreased to 6% +/- 1% (95% CI = 6%-7%) (p = 0.001 for both comparisons). There was no significant difference in mean %deltaA between gating conditions (p = 0.8); however, the 95% CI for %deltaA was decreased for deltat = 400 ms as compared to deltat = 250 ms. Both gating methods also significantly decreased %deltaA to below the reference standard of 12% +/- 1% for MA (p < 0.01 for both comparisons), suggesting that prospective cardiac gating of carotid 3DUS reduces pulsatility effects in HV and RA to levels lower than observed for much older MA patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".