COVID-19 Vaccination and New Onset Glomerular Disease: Results from the IRocGN2 International Registry
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Bibliographic record
Abstract
Key Points IgAN and MCD are the most common de novo glomerular diseases reported after COVID-19 vaccination, particularly after mRNA vaccination. Membranous nephropathy, pauci-immune GN, and collapsing GN have also been attributed to COVID-19 vaccination, some with dual histologies. Recovery of kidney function and proteinuria remission is more likely in IgAN and MCD by 4–6 months compared with the other glomerular diseases. Background Patients with de novo glomerular disease (GD) with various renal histologies have been reported after vaccination against SARS-CoV-2. Causality has not been established, and the long-term outcomes are not known. To better characterize the GDs and clinical courses/outcomes, we created the International Registry of COVID-19 vaccination and Glomerulonephritis to study in aggregate patients with de novo GN suspected after COVID-19 vaccine exposure. Methods A REDCap survey was used for anonymized data collection. Detailed information on vaccination type and timing and GD histology were recorded in the registry. We collected serial information on laboratory values (before and after vaccination and during follow-up), treatments, and kidney-related outcomes. Results Ninety-eight patients with GD were entered into the registry over 11 months from 44 centers throughout the world. Median follow-up was 89 days after diagnosis. IgA nephropathy (IgAN) and minimal change disease (MCD) were the most common kidney diseases reported. Recovery of kidney function and remission of proteinuria were more likely in IgAN and MCD at 4–6 months than with pauci-immune GN/vasculitis and membranous nephropathy. Conclusions The development of GD after vaccination against SARS-CoV-2 may be a very rare adverse event. Temporal association is present for IgAN and MCD, but causality is not firmly established. Kidney outcomes for IgAN and MCD are favorable. No changes in vaccination risk-benefit assessment are recommended based on these findings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it