Prevalence and determinants of microalbuminuria in high-risk diabetic and nondiabetic patients in the Heart Outcomes Prevention Evaluation Study. The HOPE Study Investigators.
Bibliographic record
Abstract
OBJECTIVE: To describe the characteristics of diabetic and nondiabetic participants in the Heart Outcomes Prevention Evaluation (HOPE) Study who are at high risk of developing cardiovascular (CV) disease and who have microalbuminuria (MA), and to identify the key determinants of MA in these two groups. RESEARCH DESIGN AND METHODS: Albuminuria was measured in 97% of patients enrolled in the HOPE Study as part of the MICRO-HOPE (MA, CV, and Renal Outcomes in HOPE) substudy. Baseline clinical characteristics of diabetic and nondiabetic participants with MA were recorded, and the univariate and multivariate relationship between these characteristics and the presence of MA was estimated for both groups. RESULTS: Baseline urinary albumin determinations were available in 3,574 (97.8%) diabetic participants and 5,708 (97.0%) nondiabetic participants. MA was detected in 1,151 (32.2%) diabetic participants and 837 (14.7%) nondiabetic participants. Age, waist-to-hip ratio, diabetes, smoking, hypertension, vascular disease, and left ventricular hypertrophy were independent determinants of MA in all participants. In diabetic participants, the odds of MA increased 16% for every 10.4 years of diabetes duration, and increased 8% for every 0.9% increase in glycated hemoglobin (assuming a GHb assay with an upper limit of 6% in the nondiabetic range). CONCLUSIONS: MA is independently associated with several risk factors for CV and renal disease in both diabetic and nondiabetic individuals at high risk for CV disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".