Evidence for the use of urinary albumin as marker of kidney involvement in unselected populations
Bibliographic record
Abstract
The incidence of end-stage renal disease (ESRD) has been increasing, and within a 10-year period it is predicted that it will increase by 40 %. The main cause of death in this population of more than 50,000,000 individuals worldwide is cardiovascular disease. Increased urinary albumin is a predictor of renal failure, type 1 and type 2 diabetes; it correlates closely with mean arterial pressure in hypertensive subjects, predicts cardiovascular events and has a strong association with the metabolic syndrome. Treatment with angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers can reduce progressive renal damage, the beneficial effect being partially independent of the blood pressure lowering actions. Various therapies have proved effective in reducing microalbuminuria and progressive renal damage, demonstrating that the risk factor associated with a clinical outcome decreases with appropriate treatment. Cardiovascular events are the main cause of death in most patients with chronic renal disease. Diabetes, hypertension, obesity and smoking further increase the likelihood of vascular damage. Screening target populations of people with diabetes or hypertension is well recognized. Studies in several countries that have tested for albuminuria in unselected populations have demonstrated associations between microalbuminuria and deteriorating renal function, with the risk of developing ESRD and cardiovascular outcomes. There is some evidence for the use of urinary albumin as a marker of kidney involvement in unselected populations, but this needs to be strengthened and it may be cost effective compared with no screening. This has the potential to have a major impact in developing countries facing the challenges of chronic kidney disease, diabetes and cardiovascular 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 | 0.044 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".