Microalbuminuria: Correlation With Prevalence and Severity of Coronary Artery Disease in Non-Diabetics
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that microalbuminuria (MAU) is an independent risk factor for cardiovascular diseases in diabetics, hypertensive patients and in the general population. However, the correlation of MAU with the severity of coronary artery disease (CAD) in non-diabetic patients has not been addressed in detail. This study aimed to investigate the relationship between MAU and severity of angiographically confirmed CAD in non-diabetic patients. METHODS: This was a cross-sectional study, which included 90 non-diabetic patients with documented CAD by coronary angiography. The ratio of urine albumin to creatinine was used to define MAU and severity of CAD was estimated using SYNTAX score. Patients were divided into two groups: group I that included patients without MAU and group II that included patients with MAU. RESULTS: Out of 90 non-diabetic CAD patients, 62 (68.9%) were in group I (MAU negative) and 28 (31.1%) were in group II (MAU positive). There was statistically significant difference in the median SYNTAX score between the groups (21 vs. 28, P < 0.001). The prevalences of double vessel CAD and triple vessel CAD were significantly higher in MAU positive group. There was a strong relationship between the presence of MAU and the extent and complexity of CAD (r = 0.094; P < 0.001). CONCLUSION: Thus, we conclude that patients with MAU have more severe angiographically detected CAD than those without MAU, and MAU exhibits a significant association with the presence and severity of CAD.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".