Intracranial and heart valve calcifications in hemodialysis patients—Interrelationship and clinical impact
Bibliographic record
Abstract
INTRODUCTION: Arterial calcification is an integral component of active atherosclerosis and is an independent risk factor for cardiovascular disease. Atherosclerosis is a systemic, life-threating disease that may occur at different sites and in various clinical presentations. Intracranial and valvular calcifications are common among dialysis patients and have been associated with poor cardiovascular outcomes. The aim of this study was to assess the clinical impact of valvular and intracranial arterial calcifications on mortality among chronic hemodialysis patients. METHODS: A blinded neuroradiologist graded intracranial calcifications (ICC) of all hemodialysis patients who underwent brain computerized tomography (CT) from 2015 to 2017 in our institution. Valvular calcifications were assessed by echocardiography. Only hemodialysis patients with available echocardiography and brain CT were included. FINDINGS: This study included 119 patients (mean age 70.6 ± 12.6 years, 57.1% men, and mean dialysis vintage 25.8 ± 42.6 months). Among the cohort, 19 (16%) had no cardiac or brain calcifications and 65 (54.6%) had both valvular and intracranial calcifications. Considering the patients with no calcification as the reference group yielded adjusted odds ratios for all-cause mortality of 3.68 (95%CI 1.55-8.75) among patients with any brain calcifications, p = 0.002. While valvular calcifications alone did not increase the 1-year mortality rate, ICC was the most important predictor of all-cause 1-year mortality in the study cohort. DISCUSSION: We found an independent association between ICC and the risk of death among hemodialysis patients. Assessing ICC may contribute to the risk stratification of hemodialysis patients. These calcifications are no less important than valvular calcifications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".