Baseline characteristics and enrichment results from the <scp>SONAR</scp> trial
Bibliographic record
Abstract
AIM: The SONAR trial uses an enrichment design based on the individual response to the selective endothelin receptor antagonist atrasentan on efficacy (the degree of the individual response in the urinary albumin-to-creatinine ratio [UACR]) and safety/tolerability (signs of sodium retention and acute increases in serum creatinine) to assess the effects of this agent on major renal outcomes. The patient population and enrichment results are described here. METHODS: and UACR between 300 and 5000 mg/g were enrolled. After a run-in period, eligible patients received 0.75 mg/d of atrasentan for 6 weeks. A total of 2648 responder patients in whom UACR decreased by ≥30% compared to baseline were enrolled, as were 1020 non-responders with a UACR decrease of <30%. Patients who experienced a weight gain of >3 kg and in whom brain natriuretic peptide exceeded ≥300 pg/mL, or who experienced an increase in serum creatinine >20% (0.5 mg/dL), were not randomized. RESULTS: Baseline characteristics were similar for atrasentan responders and non-responders. Upon entry to the study, median UACR was 802 mg/g in responders and 920 mg/g in non-responders. After 6 weeks of treatment with atrasentan, the UACR change in responders was -48.8% (95% CI, -49.8% to -47.9%) and in non-responders was -1.2% (95% CI, -6.4% to 3.9%). Changes in other renal risk markers were similar between responders and non-responders except for a marginally greater reduction in systolic blood pressure and eGFR in responders. CONCLUSIONS: The enrichment period has successfully identified a population with a profound UACR reduction without clinical signs of sodium retention in whom a large atrasentan effect on clinically important renal outcomes is possible. The SONAR trial aims to establish whether atrasentan confers renal protection.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".