Bibliographic record
Abstract
PURPOSE OF REVIEW: The treatment of IgA nephropathy (IgAN) has been limited by several controversies in the literature, including the benefits of corticosteroids in addition to optimized renin-angiotensin system blockers (RASBs), in those with lower estimated glomerular filtration rate (eGFR), or in different ethnic groups. Recent studies have attempted to address these issues. RECENT FINDINGS: Two observational studies suggest the efficacy of corticosteroids in those with lower eGFR, but with a higher risk of adverse events. The Supportive versus Immunosuppressive Therapy for the Treatment of Progressive IgA Nephropathy (STOP-IgAN) trial compared immunosuppression with supportive care in addition to optimized RASB, and suggests that corticosteroids (but not cyclophosphamide/azathioprine) may reduce proteinuria but the effect on renal function is not clear, that immunosuppression is associated with a high risk of adverse events and that optimal RASB is very effective at lowering proteinuria and the short-term risk of renal function decline. The Therapeutic Evaluation of Steriods in IgA Nephropathy Global (TESTING) trial compared corticosteroids with placebo in addition to optimized RASB, and demonstrated a decreased risk of renal function decline and lower proteinuria, but a higher risk of adverse events. Additional trials demonstrate the potential efficacy of enteric-budesonide but not rituximab on proteinuria reduction, and conflicting findings with mycophenolate mofetil. SUMMARY: Until less toxic therapies for IgAN are available, treatment with corticosteroids will need to be made in the context of conflicting evidence, and should likely be limited to patients at highest risk of disease progression who understand the significant risk of adverse events.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".