Anti-neutrophil cytoplasmic antibody associated glomerulonephritis complicating treatment with hydralazine
Bibliographic record
Abstract
Hydralazine, a widely used therapy for hypertension and heart failure, can elicit autoimmune disease, including anti-neutrophil cytoplasmic antibody associated glomerulonephritis (ANCA-GN). We identified 80 cases of ANCA-GN complicating treatment with hydralazine, accounting for 4.3% (80/1858 biopsies) of ANCA-GN diagnosed between 2006 and 2019. Over three-fourths of patients were on hydralazine for at least one year, with mean daily dose of approximately 250 mg/day. ANCA testing revealed p-ANCA/myeloperoxidase-ANCA seropositivity in 98%, including 39% with dual p-ANCA/myeloperoxidase-ANCA and cANCA/anti-protinase 3-ANCA positivity, often accompanied by anti-nuclear antibody (89%), anti-histone antibody (98%), and hypocomplementemia (58%). Kidney biopsy revealed necrotizing and crescentic glomerulonephritis, similar to primary ANCA-GN, but significantly less frequently pauci-immune (77 vs. 100%) and more commonly associated with mesangial hypercellularity (30 vs. 5%), electron dense deposits (62 vs. 20%), and endothelial tubuloreticular inclusions (11 vs. 0%); all significant differences. On follow-up, 42 of 51 patients received induction immunosuppression: 19 reached the combined end-points of kidney failure or death and 32 had mean creatinine of 1.49 mg/dL at last follow-up. Thus, hydralazine-associated ANCA-GN often exhibits overlapping clinical and pathologic features of mild immune complex glomerulonephritis resembling lupus nephritis. With discontinuation of hydralazine and immunosuppression, outcomes are similar to primary ANCA-GN.
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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 | 0.001 |
| 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) | 0.001 | 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".