Association of Hyperuricemia With Carotid Intima-Media Thickness, Albuminuria, Diabetes, Hypertension in Chronic Renal Failure
Bibliographic record
Abstract
Background: Carotid artery intima-media thickness (CAIMT) measured by B-mode ultrasonography is widely used as a surrogate marker atherosclerosis. Hyperuricemia is also a well recognized risk factor for cardiovascular diseases. This study was done to find out the role of hyperuricemia on CAIMT and albuminuria, diabetes and hypertension in chronic renal failure (CRF) patients. Methods: 132 CRF patients and 66 age and sex matched healthy controls were included in this study. Out of 132 CRF patients, 35 were hyperuricemic. CAIMT were measured by B-mode ultrasonography. Statistical analyses were done by SPSS (Statistical package for the social Sciences) soft ware (window version 17.0). Results: Hyperuricemia was independently correlated with CAIMT in CRF patients (P = 0.018). CRF patients with hyperuricemia had significantly higher CAIMT compared to CAIMT of CRF patients without hyperuricemia (P value < 0.001) and healthy controls (P < 0.001). In hyperuricemic CRF patients, prevalence of hypertension (P < 0.001) and diabetes (P = 0.007) was significantly higher than non-hyperuricemic CRF patients. Conclusions: Hyperuricemia is independently responsible for increased thickening of Carotid artery intima media thickness and also associated with higher level of albuminuria, and higher prevalence of hypertension and diabetes in CRF patients. doi:10.4021/wjnu14w
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".