Evaluation and Management of Chronic Kidney Disease: Synopsis of the Kidney Disease: Improving Global Outcomes 2024 Clinical Practice Guideline
Bibliographic record
Abstract
DESCRIPTION: The Kidney Disease: Improving Global Outcomes (KDIGO) organization updated its existing clinical practice guideline in 2024 to provide guidance on the evaluation, management, and treatment of chronic kidney disease (CKD) in adults and children who are not receiving kidney replacement therapy. METHODS: The KDIGO CKD Guideline Work Group defined the scope of the guideline and determined topics for systematic review. An independent Evidence Review Team systematically reviewed the evidence and graded the certainty of evidence for each of the review topics. Latest searches of the English-language literature were done in July 2023. Final modification of the guideline was informed by a public review process during summer of 2023 involving registered stakeholders. RECOMMENDATIONS: The full guideline included 28 recommendations and 141 practice points. This synopsis focuses on the recommendations that have the greatest evidence. Practice points reflect the expert opinion of the group where evidence is not that strong. Recommendations include greater emphasis on cystatin C for assessment of glomerular filtration rate, point-of-care testing in remote areas, a shift to an individualized risk-based approach to predict kidney failure, sodium-glucose cotransporter-2 inhibitors for some patients with CKD with and without diabetes, and statin use for adults older than 50 years and CKD. Together the recommendations and practice points provide guidance for how to evaluate and manage persons 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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".