European consensus-based recommendations for diagnosis and treatment of immunoglobulin A vasculitis—the SHARE initiative
Bibliographic record
Abstract
OBJECTIVES: IgA vasculitis (IgAV, formerly known as Henoch-Schönlein purpura) is the most common cause of systemic vasculitis in childhood. To date, there are no internationally agreed, evidence-based guidelines concerning the appropriate diagnosis and treatment of IgAV in children. Accordingly, treatment regimens differ widely. The European initiative SHARE (Single Hub and Access point for paediatric Rheumatology in Europe) aims to optimize care for children with rheumatic diseases. The aim therefore was to provide internationally agreed consensus recommendations for diagnosis and treatment for children with IgAV. METHODS: Recommendations were developed by a consensus process in accordance with the EULAR standard operating procedures. An extensive systematic literature review was performed, and evidence-based recommendations were extrapolated from the included papers. These were evaluated by a panel of 16 international experts via online surveys and subsequent consensus meeting, using nominal group technique. Recommendations were accepted when ⩾80% of experts agreed. RESULTS: In total, 7 recommendations for diagnosis and 19 for treatment of paediatric IgAV were accepted. Diagnostic recommendations included: appropriate use of skin and renal biopsy, renal work-up and imaging. Treatment recommendations included: the importance of appropriate analgesia and angiotensin-converting enzyme inhibitor use and non-renal indications for CS use, as well as a structured approach to treating IgAV nephritis, including appropriate use of CS and second-line agents in mild, moderate and severe disease along with use of angiotensin-converting enzyme inhibitors and maintenance therapy. CONCLUSION: The SHARE initiative provides international, evidence-based recommendations for the diagnosis and treatment of IgAV that will facilitate improvement and uniformity of care.
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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 | 0.077 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".