Early Left Ventricular Involvement Detected by Cardiovascular Magnetic Resonance Feature Tracking in Arrhythmogenic Right Ventricular Cardiomyopathy: The Effects of Left Ventricular Late Gadolinium Enhancement and Right Ventricular Dysfunction
Bibliographic record
Abstract
Background Left ventricular (LV) involvement is common in arrhythmogenic right ventricular cardiomyopathy (ARVC). We aim to evaluate LV involvement in ARVC patients by cardiovascular magnetic resonance feature tracking. Methods and Results Sixty-eight patients with ARVC and 30 controls were prospectively enrolled. ARVC patients were divided into 2 subgroups: the preserved LV ejection fraction (LVEF) group (LVEF ≥55%, n=27) and the reduced LVEF group (LVEF <55%, n=41). Cardiovascular magnetic resonance with late gadolinium enhancement (LGE) and cardiovascular magnetic resonance feature tracking were performed in all subjects. LV global and regional (basal, mid, apical) peak strain (PS) in radial, circumferential and longitudinal directions were assessed, respectively. Right ventricular global PS in three directions were also analyzed. Compared with the controls, LV global and regional PS were all significantly impaired in the reduced LVEF group (all P<0.05). However, only LV global longitudinal PS as well as mid and apical longitudinal PS were impaired in the preserved LVEF group (all P<0.05), and all these parameters were significantly associated with right ventricular global radial PS (r=-0.47, -0.47, and -0.49, respectively, all P<0.001). The reduced LVEF group showed significantly higher prevalence of LGE (95.10% versus 63.00%, P=0.002) than the preserved LVEF group. Moreover, LV radial PS was significantly reduced in LV segments with LGE (33.15±20.42%, n=46) than those without LGE (41.25±15.98%, n=386) in the preserved LVEF group (P=0.016). Conclusions In patients with ARVC, cardiovascular magnetic resonance feature tracking could detect early LV dysfunction, which was associated with LV myocardial LGE and right ventricular dysfunction.
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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 | 0.001 |
| 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".