Transforming the Care of Patients with Diabetic Kidney Disease
Bibliographic record
Abstract
Diabetes and its associated complications pose an immediate threat to humankind. Diabetic kidney disease is one of the most devastating complications, increasing the risk of death more than ten-fold over the general population. Until very recently, the only drugs proven and recommended to slow the progression of diabetic kidney disease were angiotensin-converting enzyme inhibitors and angiotensin II type 1 receptor blockers, which act by inhibiting the renin-angiotensin system. Despite their efficacy as kidney and cardiovascular protective therapies and as antihypertensive agents, renin-angiotensin system inhibitors have been grossly underutilized. Moreover, even when renin-angiotensin system inhibitors are used, patients still have a high residual risk of diabetic kidney disease progression. Finally, the kidney-protective effect of renin-angiotensin system inhibitors has been categorically demonstrated only in patients with macroalbuminuria included in the Irbesartan Diabetic Nephropathy Trial (IDNT) and Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan (RENAAL) trials, not in other individuals. The lack of new therapies to treat diabetic kidney disease over the past 2 decades has therefore represented a tremendous challenge for patients and health care providers alike. In recent years, a number of powerful new therapies have emerged that promise to transform care of patients with diabetes and kidney disease. The challenge to the community is to ensure rapid implementation of these treatments. This white paper highlights advances in treatment, opportunities for patients, challenges, and possible solutions to advance kidney health, and introduces the launch of the Diabetic Kidney Disease Collaborative at the American Society of Nephrology, to aid in accomplishing these goals.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".