MP347CALCITRIOL ALTERS THE PATHOLOGICAL PHENOTYPE ASSOCIATED WITH LEFT VENTRICULAR HYPERTROPHY IN AN EXPERIMENTAL MODEL OF CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Introduction and Aims: Left ventricular hypertrophy (LVH) is prevalent and a risk factor for CVD-related death in the chronic kidney disease(CKD) population. LVH has been associated with vascular calcification (VC), vascular stiffness and high levels of FGF-23 in humans and in animal models of CKD. In Canada, approximately 50% of patients with ESKD receive calcitriol, active vitamin D3, for the management of secondary hyperparathyroidism. Calcitriol treatment increases FGF-23 levels and has been associated with increased VC and vascular stiffness in experimental CKD. We examined the impact of 2 doses of calcitriol on LVH in an experimental model of CKD. Methods: Progressive CKD was induced in Sprague-Dawley rats (N=61, 14 weeks) using a 0.25% adenine diet for 8 weeks. After 3 weeks of diet, the cohort was stratified by serum creatinine into three daily treatment doses of calcitriol: 0 ng/kg (CKD control), 20 ng/kg, or 80ng/kg (N=20-22/group). At sacrifice, vascular stiffness was assessed by pulse wave velocity (PVW) and left ventricular mass index (LVMI) was calculated by LV weight normalized to both bodyweight and tibia length. VC in the aorta was assessed by calcium content and confirmed with von Kossa staining. Plasma levels of FGF-23(C-terminal) and iPTH were assessed by ELISA. Serum levels of creatinine, phosphate and calcium concentration were measured.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".