Prevalence of left ventricle non-compaction criteria in adult patients with bicuspid aortic valve versus healthy control subjects
Bibliographic record
Abstract
Objective The aim of this study was to compare the prevalence of left ventricle non-compaction (LVNC) criteria (or hypertrabeculation) in a cohort of patients with bicuspid aortic valve (BAV) and healthy control subjects (CTL) without cardiovascular disease using cardiovascular MR (CMR). Methods 79 patients with BAV and 85 CTL with tricuspid aortic valve and free of known cardiovascular disease underwent CMR to evaluate the presence of LVNC criteria. The left ventricle was assessed at end-systole and end-diastole, in the short-axis, two-chamber and four-chamber views and divided into the 16 standardised myocardial segments. LVNC was assessed using the non-compacted/compacted (NC/C) myocardium ratio and was considered to be present if at least one of the myocardial segments had a NC/C ratio superior to the cut-off values defined in previous studies: Jenni et al (>2.0 end-systole); Petersen et al (>2.3 end-diastole); or Fazio et al (>2.5 end-diastole). Results 15 CTL (17.6%) vs 8 BAV (10.1%) fulfilled Jenni et al ’s criterion; 69 CTL (81.2%) vs 49 BAV (62.0%) fulfilled Petersen et al ’s criterion; and 66 CTL (77.6%) vs 43 BAV (54.4%) fulfilled Fazio et al ’s criterion. Petersen et al and Fazio et al ’s LVNC criteria were met more often by CTL (p=0.006 and p=0.002, respectively) than patients with BAV, whereas this difference was not statistically significant according to Jenni et al ’s criterion (p=0.17). In multivariable analyses, after adjusting for age, sex, the presence of significant valve dysfunction (>mild stenosis or >mild regurgitation), indexed LV mass, indexed LV end-diastolic volume and LV ejection fraction, BAV was not associated with any of the three LVNC criteria. Conclusion Patients with BAV do not harbour more LVNC than the general population and there is no evidence that they are at higher risk for the development of LVNC cardiomyopathy.
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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 | 0.000 |
| 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 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".