Left Atrial 4D Flow Characteristics in Patients with Atrial Fibrillation: Comparison with Healthy Controls and Associations with Left Atrial Remodelling and Contractile Health
Bibliographic record
Abstract
Left atrial (LA) four-dimensional (4D) flow quantification and strain may help predict stroke and thrombus formation in patients with atrial fibrillation (AF). We aimed to characterize flow changes in AF patients undergoing pulmonary vein isolation (PVI) and their associations with LA remodelling and contractility markers. Fifty-seven consecutive patients referred for magnetic resonance imaging before first-time PVI and twelve healthy volunteers (HV). LA velocity and stasis maps were obtained from 4D flow. Cine images were used for LA volume, ejection fraction, and strain. Patients’ age was 60 ± 9 years (25% female), versus 44 ± 15 years in HV (8% female). LA 4D flow markers showed LA stasis was reduced in AF patients (median [Q1, Q3] 45.0% (36.0, 54.0) vs. 34.5% (22.8, 45.3); p = 0.040). LA stasis and mean LA velocity were associated in AF patients (r = −0.52; p < 0.001) and HV (r = −0.9; p < 0.001). Associations were poorer for peak LA velocity in AF patients (r = −0.39; p = 0.003). LA stasis was not associated with any marker of LA contractile function in either the AF or HV cohorts. In conclusion, compared with HV patients, patients with AF showed greater LA stasis on 4D flow. LA stasis was not associated with markers of LA contractile function.
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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 | 0.000 | 0.001 |
| 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".