Diabetes Management in Chronic Kidney Disease: Synopsis of the KDIGO 2022 Clinical Practice Guideline Update
Bibliographic record
Abstract
DESCRIPTION: The KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease is an update of the 2020 guideline from Kidney Disease: Improving Global Outcomes (KDIGO). METHODS: The KDIGO Work Group updated the guideline, which included reviewing and grading new evidence that was identified and summarized. As in the previous guideline, the Work Group used the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach to appraise evidence and rate the strength of recommendations and expert judgment to develop consensus practice points. New evidence led to updating of recommendations in the chapters Comprehensive Care in Patients With Diabetes and CKD (Chapter 1) and Glucose-Lowering Therapies in Patients With T2D and CKD (Chapter 4). New evidence did not change recommendations in the chapters Glycemic Monitoring and Targets in Patients With Diabetes and CKD (Chapter 2), Lifestyle Interventions in Patients With Diabetes and CKD (Chapter 3), and Approaches to Management of Patients With Diabetes and CKD (Chapter 5). RECOMMENDATIONS: The updated guideline includes 13 recommendations and 52 practice points for clinicians caring for patients with diabetes and chronic kidney disease (CKD). A focus on preserving kidney function and maintaining well-being is recommended using a layered approach to care, starting with a foundation of lifestyle interventions, self-management, and first-line pharmacotherapy (such as sodium-glucose cotransporter-2 inhibitors) demonstrated to improve clinical outcomes. To this are added additional drugs with heart and kidney protection, such as glucagon-like peptide-1 receptor agonists and nonsteroidal mineralocorticoid receptor antagonists, and interventions to control risk factors for CKD progression and cardiovascular events, such as blood pressure, glycemia, and lipids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".