Impact of arterial microcalcification of the vascular access on cardiovascular mortality in hemodialysis patients
Bibliographic record
Abstract
Gross vascular calcification seen on imaging studies is common in hemodialysis (HD) patients, and is a significant predictor for cardiovascular mortality in HD patients. We have reported that arterial microcalcification (AMiC) of the vascular access is associated with increased aortic stiffness. This study investigated the impact of vascular access AMiC on cardiovascular mortality in HD patients. The study included 149 HD patients (mean age: 59.1 ± 13.9 years, 86 men and 63 women, 65.8% diabetic) who underwent vascular access surgery. Radial or brachial artery specimens were obtained intraoperatively, and pathologic examination was performed using von Kossa stain to identify AMiC. We compared all-cause and cardiovascular mortality between patients with and without AMiC. The mean follow-up was 37.8 ± 34.5 months, and AMiC was present in 38.8% (n = 57) of patients. The presence of diabetes (odds ratio: 16.49, 95% confidence interval: 1.81-150.36, P = 0.013) was the only independent risk factor for vascular access AMiC. During the observational period, there were 27 cardiovascular deaths. Kaplan-Meier analysis showed an increased cardiovascular mortality risk (log rank = 4.83, P = 0.028) in AMiC patients, and Cox regression analysis confirmed that AMiC was an independent predictor for cardiovascular mortality (hazard ratio: 2.35, 95% confidence interval: 1.09-5.09, P = 0.030). In conclusion, vascular access AMiC is a strong risk factor for cardiovascular mortality in 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 | 0.000 | 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 | 0.000 | 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".