Executive summary of the 2020 KDIGO Diabetes Management in CKD Guideline: evidence-based advances in monitoring and treatment
Bibliographic record
Abstract
THE KIDNEY DISEASE: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease represents the first KDIGO guideline on this subject. The guideline comes at a time when advances in diabetes technology and therapeutics offer new options to manage the large population of patients with diabetes and chronic kidney disease (CKD) at high risk of poor health outcomes. An enlarging base of high-quality evidence from randomized clinical trials is available to evaluate important new treatments offering organ protection, such as sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists. The goal of the new guideline is to provide evidence-based recommendations to optimize the clinical care of people with diabetes and CKD by integrating new options with existing management strategies. In addition, the guideline contains practice points to facilitate implementation when insufficient data are available to make well-justified recommendations or when additional guidance may be useful for clinical application. The guideline covers comprehensive care of patients with diabetes and CKD, glycemic monitoring and targets, lifestyle interventions, antihyperglycemic therapies, and self-management and health systems approaches to management of patients with diabetes and 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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.047 |
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".