Effect of pharmacological suppression of secondary hyperparathyroidism on cardiovascular hemodynamics in predialysis CKD patients: A preliminary observation
Bibliographic record
Abstract
Cardiovascular events are the principal cause of mortality in patients with chronic kidney disease (CKD). Secondary hyperparathyroidism (SHPT), a common complication of CKD, contributes to cardiac dysfunction. This study is an attempt to demonstrate the effects of parathyroid hormone suppression with oral calcitriol on cardiovascular hemodynamics. Twenty predialysis CKD patients with SHPT were given calcitriol therapy for 12 weeks. Ten similar patients received placebo. Echocardiographic assessment of cardiac function was performed at baseline and after 12 weeks of treatment. Calcitriol therapy effectively suppressed SHPT. Baseline left ventricular (LV) end diastolic diameter and LV end systolic diameter were 4.86+/-0.48 and 2.86+/-0.33 cm, and the mean FS was 41.02+/-4.79%. Left ventricular end systolic and end diastolic volumes were normal (42.30+/-9.07 and 91.40+/-19.68 mL). The ejection fraction was slightly reduced (53.54+/-3.57%). Pretreatment Doppler indices including E velocity (0.816+/-0.087 m/s), A velocity (0.696+/-0.089 m/s), and E/A ratio (1.193+/-0.210) were significantly impaired. After 12 weeks of calcitriol therapy, there was no significant change in the LV dimensions or ejection fraction, but there was a significant improvement in the diastolic parameters, namely the A velocity (0.680+/-0.084) and E/A ratio (1.238+/-0.180). Secondary hyperparathyroidism is an important factor in the pathogenesis of cardiovascular complications in CKD. There is evidence to support that correction of hyperparathyroidism can improve the systolic dysfunction seen in advanced kidney disease. This study shows that diastolic dysfunction seen in predialysis CKD patients may also be possibly improved with calcitriol therapy.
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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 | 0.001 |
| 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) | 0.001 | 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".