Rapid and Simultaneous Initiation of Guideline-Directed Kidney Therapies in Patients with CKD and Type 2 Diabetes
Bibliographic record
Abstract
The global incidence of CKD continues to rise, with type 2 diabetes as a major contributor. At any stage of CKD, patients with concurrent CKD and type 2 diabetes are at heightened cardiovascular risk and have a greater likelihood of dying from cardiovascular causes than progressing to kidney failure. Consequently, the use of "four pillars" of CKD therapy, including renin-angiotensin system inhibitors, sodium-glucose cotransporter 2 inhibitor, nonsteroidal mineralocorticoid receptor antagonists, and glucagon-like peptide-1 receptor agonists, has been advocated to reduce cardiovascular-kidney risk. Although these therapies can mitigate cardiovascular and kidney events when used individually, the residual risks of these events remain high across major clinical trials testing these therapies separately as well as in real-world clinical settings. This raises the question about when to optimally initiate these therapies, including strategies that start these agents in rapid sequence, or even simultaneously, to reduce long-term risk, thereby mirroring best practices with rapid titration schedules in patients with heart failure. However, initiating all four therapies simultaneously in the setting of CKD has not yet been tested due to lack of data on safety and tolerability in this high-risk population. Data regarding the safety profile of rapid sequence initiation remain limited. Therefore, our aim was to review the existing evidence on the safety profiles of guideline-recommended therapies and discuss the challenges associated with rapid sequence initiation of these treatments in patients with CKD.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".