A Pathway to High Quality Clinical Trials in IgA Vasculitis Nephritis: Meeting Proceedings From a Multiprofessional International Collaborative Workshop
Bibliographic record
Abstract
IgA vasculitis (IgAV) is an autoimmune disease that affects the small vessels of the skin, joints, gastrointestinal (GI) tract, and kidneys. In the long term, IgAV associated with nephritis (IgAV-N) can progress to kidney failure. Evidence-based clinical studies of IgAV-N are few, leading to huge variations in treatment approaches and suboptimal outcomes. The wealth of emerging efficacious treatments for IgA nephrology brings new opportunities to this disease. The aim of this report is to describe the proceedings of a multiprofessional collaborative workshop convened to identify the barriers to developing high quality evidence for patients with IgAV-N. A multiprofessional group consisting of 53 attendees from 13 countries met. The meeting was represented by a variety of professional backgrounds, including lay attendees, with different levels of expertise (32% professors and 19% midcareer doctors). Using predefined aims, key themes were extracted, and an action plan developed. Consensus was obtained that there is sufficient similarity between adults and children in terms of the organs involved, pathophysiology, histological features, and likely response to treatment. Important differences included the greater spontaneous improvement in children and worse kidney outcomes in some populations. It was agreed that patients at greatest risk of kidney failure should be the primary focus of initial clinical trials. Important considerations included the following: diagnostic classification for adult onset IgAV, observational data, evidence of scientific similarity to IgA nephropathy (IgAN), an age-inclusive approach to trial design, systemic disease secondary end points, and the inclusion of patient-reported outcomes. This manuscript communicates an expert-informed pathway to high-quality evidence for IgAV-N.
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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 | 0.500 | 0.474 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.010 | 0.060 |
| Research integrity | 0.027 | 0.034 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".