Executive summary of the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease: known knowns and known unknowns
Bibliographic record
Abstract
The Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease (CKD) updates the KDIGO 2012 guideline and has been developed with patient partners, clinicians, and researchers around the world, using robust methodology. This update, based on a substantially broader base of evidence than has previously been available, reflects an exciting time in nephrology. New therapies and strategies have been tested in large and diverse populations that help to inform care; however, this guideline is not intended for people receiving dialysis nor those who have a kidney transplant. The document is sensitive to international considerations, CKD across the lifespan, and discusses special considerations in implementation. The scope includes chapters dedicated to the evaluation and risk assessment of people with CKD, management to delay CKD progression and its complications, medication management and drug stewardship in CKD, and optimal models of CKD care. Treatment approaches and actionable guideline recommendations are based on systematic reviews of relevant studies and appraisal of the quality of the evidence and the strength of recommendations which followed the "Grading of Recommendations Assessment, Development, and Evaluation" (GRADE) approach. The limitations of the evidence are discussed. The guideline also provides practice points, which serve to direct clinical care or activities for which a systematic review was not conducted, and it includes useful infographics and describes an important research agenda for the future. It targets a broad audience of people with CKD and their healthcare, while being mindful of implications for policy and payment.
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 | 0.015 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.055 |
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".