Left ventricular structure and diastolic function by cardiac magnetic resonance imaging in hypertrophic cardiomyopathy
Bibliographic record
Abstract
OBJECTIVE: Diastolic dysfunction is common in hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD), but its relationships with left ventricular (LV) parameters have not been well studied. Our objective was to assess the relationship of various measures of diastolic function, and maximum left ventricular wall thickness (MLVWT) and left ventricular mass index (LVMI) in HCM, HHD and normal controls using cardiac magnetic resonance imaging (CMR). We also assessed LV parameters and diastolic function in relation to late gadolinium enhancement (LGE) and right ventricular (RV) hypertrophy in HCM. METHODS: 41 patients with HCM, 21 patients with HHD and 20 controls were studied. Peak filling rate (PFR), time to peak filling (TPF), MLVWT and LVMI were measured using CMR. LGE and RV morphology were assessed in HCM patients. RESULTS: MLVWT correlated with TPF in HCM (r=0.38; p=0.02), HHD (r=0.58; p=0.01) and controls (r=0.54; p=0.01); correlation between MLVWT and TPF was weaker in HCM than HHD. LVMI did not correlate with diastolic function. In HCM, LGE extent correlated with MLVWT (τ=0.41; p=0.002) and with TPF (τ=0.29; p=0.02). The HCM patients with RV hypertrophy had higher MLVWT (p<0.001) and TPF (p=0.03) than patients without RV hypertrophy. CONCLUSION: MLVWT correlates with diastolic function (TPF) in HCM, HHD and controls. LVMI did not show significant correlation with TPF. The diastolic dysfunction in HCM is not entirely explained by wall thickening. LGE and RV involvement are associated with worse LV diastolic function, suggesting that these may be markers of more severe underlying myocardial disarray and fibrosis that contribute to diastolic dysfunction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".