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Record W4415549456 · doi:10.1681/asn.20252dzef6w2

Therapies for IgAN in the Cure Glomerulonephropathy Network (CureGN)

2025· article· en· W4415549456 on OpenAlexaff
Terri Madison, Christopher Ngai, Margaret Helmuth, Caroline Smerdon, Andrew S. Bomback, Heather N. Reich, Laura H. Mariani, Myda Khalid

Bibliographic record

VenueJournal of the American Society of Nephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsKidney diseaseNephropathyNephrologyMEDLINEGlomerulonephritis

Abstract

fetched live from OpenAlex

Background: There is sparse literature on real-world use and impact of novel emerging therapies approved to treat IgAN and current trends in immunosuppressive therapy (IST) use. In North American adults with IgAN enrolled in CureGN within 6 months of their diagnostic kidney biopsy, we report treatment patterns and associated disease trajectories. Methods: CureGN is a prospective registry of participants with glomerular disease. Descriptive statistics estimated frequency of IgAN treatments, number of IST classes used, and time from biopsy to first IST/systemic corticosteroid (SCS). Cox proportional hazard models assessed risk of a composite 40% decline/kidney failure by IST and SCS use. Results: 152 adults with incident IgAN were followed for a median of 6.8 years (interquartile range [IQR]: 2.4, 8.1). A majority were male (64%), white (74%), and non-Hispanic (84%). IST were the most frequently used IgAN medications and 61% specifically used SCS. Post-enrollment, the most common IgAN medications were renin-angiotensin system (RAS) inhibitors, other ISTs, and sodium glucose cotransporter-2 inhibitors (SGLT2i), with 29% of participants receiving 2+ IST classes. Few participants (n=5) received newer IgAN therapies (eg, sparsentan, targeted delayed-release budesonide). (Table 1) Compared with participants with estimated glomerular filtration rate (eGFR) ≥60 at biopsy, those with eGFR <60 were more likely to have received 2+ IST classes during follow-up (37.1% vs 16.3%, p=0.03). Urine protein:creatinine ratio (UPCR) at biopsy was not associated with number of IST classes used or time to first IST. Adjusting for eGFR and UPCR at enrollment, IST use was not associated with a decreased risk of progression to kidney failure (Table 2). Conclusion: The impact oftraditional IST on long term kidney function requires further exploration. Despite availability of novel IgAN therapies, conservative management (RAS inhibition, SGLT2i) and traditional IST remain most used. Funding: Commercial Support - Calliditas NA Enterprises Inc.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.012
GPT teacher head0.294
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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