Study Design and Baseline Characteristics of ALIGN, a Randomized Controlled Study of Atrasentan in Patients With IgA Nephropathy
Bibliographic record
Abstract
Introduction Endothelin A (ET A ) receptor activation is a driver of proteinuria, kidney inflammation and fibrosis in IgA nephropathy (IgAN). Atrasentan, a selective ET A receptor antagonist, has potential to reduce proteinuria and preserve kidney function in IgAN. ALIGN (NCT04573478) is a phase 3, randomized, double-blind, placebo-controlled clinical trial of atrasentan in patients with IgAN at high risk of kidney function loss. Methods Eligibility criteria for the ALIGN main stratum included patients with biopsy-proven IgAN, total protein excretion ≥1g/d, eGFR ≥30 mL/min/1.73m 2 , receiving a maximally-tolerated stable dose of a renin-angiotensin system inhibitor (RASi). An exploratory stratum enrolled participants also receiving a stable dose of sodium glucose cotransporter-2 inhibitors (SGLT2i). Participants were randomized to 0.75 mg atrasentan or placebo orally daily for 132 weeks followed by a 4-week wash-out period. The primary outcome is proteinuria change from baseline (24h urine protein–creatinine ratio UPCR) to Week 36 in the main stratum. Key secondary endpoints include eGFR change from baseline to Week 136, safety and tolerability. Results Enrolment occurred across 20 countries; 404 patients are randomized: 340 in the main stratum and 64 in the SGLT2i stratum. At baseline in the main stratum, mean age was 44.7 years, 42.4% were female, mean eGFR and median UPCR were 58.9 mL/min/1.73 m 2 and 1.4 g/g, respectively. Conclusion The ongoing ALIGN trial evaluates the efficacy and safety of atrasentan in a representative global IgAN population at risk for disease progression despite optimized RASi therapy and includes an exploratory stratum evaluating atrasentan use in combination with an SGLT2i.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".