Early diabetic nephropathy in type 1 diabetes
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite improvements in glycemic and blood pressure control in patients with type 1 diabetes, diabetic nephropathy remains the most common cause of chronic kidney disease worldwide. A major challenge in preventing diabetic nephropathy is the inability to identify high-risk patients at an early stage, emphasizing the importance of discovering new therapeutic targets and implementation of clinical trials to reduce diabetic nephropathy risk. RECENT FINDINGS: Limitations of managing patients with diabetic nephropathy with renin-angiotensin-aldosterone system blockade have been identified in recent clinical trials, including the failure of primary prevention studies in T1D and the demonstration of harm with dual renin-angiotensin-aldosterone system blockade. Fortunately, several new targets, including serum uric acid, insulin sensitivity, vasopressin, and sodium-glucose cotransporter-2 inhibition, are promising in the prevention and treatment of diabetic nephropathy. SUMMARY: Diabetic nephropathy is characterized by a long clinically silent period without signs or symptoms of disease. There is an urgent need for improved methods of detecting early mediators of renal injury, to ultimately prevent the initiation and progression of diabetic nephropathy. In this review, we will focus on early diabetic nephropathy and summarize potential new therapeutic targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".