ECG-gated MR angiography at 3T for follow-up after surgery involving the ascending aorta
Bibliographic record
Abstract
We aimed to evaluate electrocardiogram (ECG)-gated MR angiography (MRA) in the follow-up after surgery involving the ascending aorta regarding technical feasibility, image quality, spectrum of findings, and their implications for clinical management. We retrospectively analyzed a cohort of 19 patients (median age 59 years, range 38-79 years), who underwent MRA for follow-up imaging after surgery involving the ascending aorta. Our magnetic resonance imaging protocol consisted of a time-resolved, non-ECG-gated MRA and an ECG-gated MRA performed at 3T. Median examination duration was 25 minutes (range 11-41 minutes). All examinations were assessed by 2 readers in consensus for image quality on a 5-point scale ranging from 1 (non-diagnostic) to 5 (excellent). MRA examinations and patient charts were analyzed for diagnostic findings and their consequences for further management. Subjective image quality was rated as "sufficient" (score 3.1 ± 1.1) for the aortic root and as "good" to "excellent" for the ascending aorta (score 4.5 ± 0.7), aortic arch (4.5 ± 0.7), supra-aortic branches (4.5 ± 0.6) and descending aorta (4.6 ± 0.7). Abnormal findings were seen in 6 patients (32%) including progressive diameter of remaining aneurysm or dissection (3 patients, 16%) and suture aneurysms (3 patients, 16%). In all 6 of these patients, abnormal findings at MRA had consequences for clinical management. ECG-gated MR angiography at 3T yields good image quality for post-operative surveillance after aortic surgery involving the ascending aorta. This technique may serve as an alternative to computed tomography particularly in younger patients with repeated follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".