Moving from Evidence to Implementation of Breakthrough Therapies for Diabetic Kidney Disease
Bibliographic record
Abstract
Diabetic kidney disease is the most frequent cause of kidney failure, accounting for half of all cases worldwide. Moreover, deaths from diabetic kidney disease increased 106% between 1990 and 2013, with most attributed to cardiovascular disease. Recommended screening and monitoring for diabetic kidney disease are conducted in less than half of patients with diabetes. Standard-of-care treatment with an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker is correspondingly low. Sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, and a nonsteroidal mineralocorticoid antagonist are highly effective therapies to reduce kidney and cardiovascular risks in diabetic kidney disease. However, <20% of eligible patients are receiving these agents. Critical barriers are high out-of-pocket drug costs and low reimbursement rates. Data demonstrating clinical and cost-effectiveness of diabetic kidney disease care are needed to garner payer and health care system support. The pharmaceutical industry should collaborate on value-based care by increasing access through affordable drug prices. Additionally, multidisciplinary models and communication technologies tailored to individual health care systems are needed to support optimal diabetic kidney disease care. Community outreach efforts are also central to make care accessible and equitable. Finally, it is imperative that patient preferences and priorities shape implementation strategies. Access to care and implementation of breakthrough therapies for diabetic kidney disease can save millions of lives by preventing kidney failure, cardiovascular events, and premature death. Coalitions composed of patients, families, community groups, health care professionals, health care systems, federal agencies, and payers are essential to develop collaborative models that successfully address this major public health challenge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.155 | 0.459 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 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".