The relationship between serum uric acid level and carotid intima‐media thickness in hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate the relationship between carotid intima-media thickness (CIMT), which is a known indicator of cardiovascular risk and atherosclerosis, and uric acid level, which may be an easy marker for cardiovascular diseases due to its antioxidant and pro-oxidant properties in hemodialysis patients. METHODS: In this cross-sectional study, we evaluated 77 hemodialysis patients. The mean CIMT of these patients was measured and recorded by Doppler ultrasonography. Patients were divided into two groups according to their serum uric acid levels. Correlation analysis and linear regression analysis were used to define the relationship between study parameters. FINDINGS: The mean CIMT levels in the normouricemic group and the hyperuricemic group were 0.95 ± 0.15 and 1.07 ± 0.15, respectively. There was a statistically significant difference between the two groups (p = 0.001). There was a statistically significant and moderate linear correlation between serum uric acid level and mean CIMT (r = 0.402; p = 0.002). Univariate and multivariate linear regression analyses were performed to identify variables that could independently affect the mean CIMT value. According to analysis, uric acid (p < 0.001), hypertension (p = 0.008), albumin (p = 0.029), and C-reactive protein (p = 0.042) were found independent risk factors for mean CIMT value. DISCUSSION: We found a significant relationship between serum uric acid level and CIMT, which indicates carotid atherosclerosis. Serum uric acid level is a low-cost laboratory parameter that can be measured in almost all laboratories, and it may be valuable in the hemodialysis patient group to identify patients at high risk of carotid atherosclerosis.
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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 | 0.000 |
| 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".