Executive summary of the KDIGO 2025 Clinical Practice Guideline for the Management of Immunoglobulin A Nephropathy (IgAN) and Immunoglobulin A Vasculitis (IgAV)
Bibliographic record
Abstract
The Immunoglobulin A nephropathy (IgAN) and Immunoglobulin A vasculitis (IgAV) management guideline was last updated and published as part of the Kidney Disease: Improving Global Outcomes (KDIGO) 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Based on new developments in IgAN assessment and therapy, a major update of the guideline was necessary in 2024. Compared with the last version, the KDIGO 2025 IgAN guideline now encourages a more liberal kidney biopsy policy and suggests aiming for stricter proteinuria control, with a goal of <0.5 g/d, ideally <0.3 g/d, and a stable estimated glomerular filtration rate. A major new concept in the 2025 guideline is to initiate treatment with (i) therapies that prevent or reduce pathogenic IgA production and IgA/IgA and IgA/IgG immune complex formation along with (ii) therapies to manage the consequences of existing IgAN-induced nephron loss. Approaches to achieve the first aim are currently limited to targeted-release budesonide (Nefecon) or reduced-dose systemic corticosteroid therapy and, in Chinese patients, mycophenolate mofetil. Approaches to the more generic second aim include healthy lifestyle education, renin-angiotensin system blockers, sodium-glucose cotransporter-2 inhibitors, and/or dual endothelin angiotensin receptor blockers. Little has changed for special situations of IgA-dominant immune complex glomerular diseases such as nephrotic syndrome, acute kidney injury, rapidly progressive glomerulonephritis, and pregnancy in IgAN, or children with IgAN or IgAV, given the lack of major clinical trials in these patient populations. Here, we provide an executive summary of the most important changes in the KDIGO 2025 IgAN and IgAV guideline as a quick reference.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.048 |
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".