Relapses or de-novo IgA nephropathy following COVID-19 vaccination; a narrative review
Bibliographic record
Abstract
Immunoglobulin A (IgA) nephropathy is the most common type of glomerulonephritis worldwide characterized by excessive serum levels of glycosylated which triggers the generation of glycan-specific IgG and IgA autoantibodies. This pathological condition results in the formation of circulatory IgA immune complexes, which are essential for the development of glomerular inflammation, especially IgA nephropathy. The serum galactosylated IgA1, IgG, and IgA autoantibodies are suggested as the biomarkers of IgA nephropathy since IgA antibodies are early markers for disease activity too. Serum IgA antibodies emerged as the early COVID-19-specific antibody response about two days after initial symptoms of COVID-19 in comparison with IgG and IgM antibody concentrations, which appeared after five days. IgA nephropathy is frequently presented as microscopic or macroscopic hematuria and proteinuria with a male predominance. COVID-19 infection can include several organs aside from the lungs, such as kidneys through different mechanisms. It is demonstrated in most cases that short-lasting symptoms such as gross hematuria resolve either spontaneously or following a short course of steroids. This review summarized the reported cases of relapses or de-novo reported cases of relapses or de-novo IgA nephropathy and IgA vasculitis following COVID-19 vaccination.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".