KDIGO 2025 clinical practice guideline for the evaluation, management, and treatment of autosomal dominant polycystic kidney disease (ADPKD): executive summary
Bibliographic record
Abstract
The Kidney Disease: Improving Global Outcomes (KDIGO) 2025 Clinical Practice Guideline for the Evaluation, Management, and Treatment of Autosomal Dominant Polycystic Kidney Disease (ADPKD) represents the first KDIGO guideline on this subject. Its scope includes nomenclature, diagnosis, prognosis, and prevalence; kidney manifestations; chronic kidney disease (CKD) management and progression, kidney failure, and kidney replacement therapy; therapies to delay progression of kidney disease; polycystic liver disease; intracranial aneurysms and other extrarenal manifestations; lifestyle and psychosocial aspects; pregnancy and reproductive issues; pediatric issues; and approaches to the management of people with ADPKD. The guideline has been developed with patient partners, clinicians, and researchers around the world, with the goal to generate a useful resource for healthcare providers and patients by providing actionable recommendations. The development of this guideline followed an explicit process of evidence review and appraisal, based on a rigorous, formal systematic literature review. The strength of recommendations follows the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The guideline also provides practice points serving to direct clinical care or activities relating to areas for which a systematic review was not conducted. Limitations of the evidence are discussed. Research recommendations to address gaps in knowledge, and implications for policy and payment, are provided. The guideline targets a broad audience of healthcare providers, people living with ADPKD, and stakeholders involved in the various aspects of ADPKD care.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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".