Cardiovascular disease on hemodialysis: Predictors of atherosclerosis and survival
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of mortality in hemodialysis (HD) patients. This could not be explained by the known traditional CVD risk factors. In this study, we attempted to elucidate the factors influencing atherosclerosis, as measured by carotid artery intima-media thickness (IMT), in HD patients and their impact on cardiovascular mortality. A cohort of 50 patients started on HD was selected for this study. At baseline, IMT and the presence of atheromatous plaques were assessed. Plasma homocysteine (Hcy), malondialdehyde, total antioxidant capacity, von Willebrand factor, vitamins C, E, B(6), B(12), folate, and C-reactive protein (CRP) were also measured. Patients were followed up for 2 years to determine the impact of IMT and associated markers on mortality using survival analysis as well as Cox proportional hazard. At baseline, 40% of the patients had IMT>0.8 mm. They were older, had higher CRP (P<0.001), and lower serum albumin (P=0.03). Intima-media thickness >0.8 mm was associated with high calcium (risk ratio [RR]: 6.06; confidence interval [CI]: 0.75-12.25) and CRP (RR: 10.94 [CI: 2.56-46.74]). Fifteen patients (30%) died during the 2-year follow-up; the main cause of death was CVD (42%). The relative risk mortality was high with increased IMT (RR: 120.04 [CI: 4.18-3445.9]), Index of Coexistent Disease for CVD (RR: 4.04 [CI: 1.92-8.5]), and plasma Hcy (RR: 1.08 [CI: 1.02-1.13]). Markers of inflammation and increased serum calcium were significant predictors of increased carotid artery IMT. High IMT, Index of Coexistent Disease, and Hcy were associated with a high RR of all-cause mortality among a cohort of HD patients.
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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 | 0.002 |
| 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 | 0.001 | 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".