Executive summary of the KDIGO 2025 Clinical Practice Guideline for the Management of Nephrotic Syndrome in Children
Bibliographic record
Abstract
The Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for the Management of Glomerular Diseases was last updated and published in 2021. KDIGO continues to be committed to the nephrology community to provide periodic updates, based on new developments for each of the glomerular diseases. For children with nephrotic syndrome, the updated guideline now contains a treatment algorithm on when to perform a kidney biopsy and/or genetic testing and which immunosuppressive therapy to use in children with a complete response to glucocorticoids (steroid sensitive), who subsequently become infrequent or frequent relapsers or even steroid dependent. If a glucocorticoid-sparing agent must be considered after failure of an initial glucocorticoid therapy to induce remission, the choice among a calcineurin inhibitor, oral cyclophosphamide, levamisole, mycophenolate mofetil, and rituximab is a decision that requires consideration of patient-related issues such as resources, adherence, adverse effects, and patient preferences. Herein, an executive summary of the most important changes in the KDIGO 2025 Clinical Practice Guideline for the Management of Nephrotic Syndrome in Children is provided 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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".