Cardiovascular Disease Risk Factors and Left Ventricular Hypertrophy in Girls and Boys With CKD
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Prior studies suggested that women with CKD have higher risk for cardiovascular disease (CVD) and mortality than men, although putative mechanisms for this higher risk have not been identified. We assessed sex differences in (1) CVD risk factors and left ventricular hypertrophy (LVH), and (2) the relationship of left ventricular mass (LVM) with different measures of body size in children with CKD. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: >95th percentile and LVM relative to estimated lean body mass (eLBM) >95th percentile for age and sex. Differences in LVM by sex were assessed by adjusting for age, weight, height, and eLBM using bivariate and multivariate regression models. RESULTS: Girls were less likely to have uncontrolled hypertension (26% versus 38%, P=0.001), had lower diastolic BP z-scores (+0.3 versus +0.6, P=0.001), and had lower prevalence of high triglycerides (38% versus 47%, P=0.03) compared with boys. When LVH was defined by LVM indexed to height, girls had higher prevalence of LVH (16% versus 9%, P=0.01); when LVH was defined by LVM relative to eLBM, prevalence of LVH was similar between girls and boys (18% versus 17%, P=0.92). In regression models adjusting for eLBM, no sex differences in LVM were observed. CONCLUSIONS: Despite lack of increased prevalence of CVD risk factors, indexing LVM to height showed a higher proportion of LVH among girls, while estimates of LVH based on eLBM showed no sex differences. Indexing LVM to eLBM may be an alternative to height indexing in children with CKD.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".