Renal-Limited Antiglomerular Basement Membrane Disease Related To Alemtuzumab: A Case Report
Bibliographic record
Abstract
RATIONALE: Alemtuzumab is a monoclonal antibody approved for the treatment of relapsing-remitting multiple sclerosis (RRMS). Many autoimmune-mediated adverse events have been associated with alemtuzumab, including renal-limited anti-glomerular basement membrane (GBM) disease. PRESENTING CONCERN: A 52-year-old female with RRMS presented with acute kidney injury 39 months after receiving 1 cycle of alemtuzumab. She had a history of alemtuzumab-associated hypothyroidism and thrombocytopenia, urinary tract infections, and chronically abnormal urinalyses. DIAGNOSIS: A diagnosis of renal-limited anti-GBM disease was made based on renal biopsy and positive anti-GBM serology. Alemtuzumab was thought to be the trigger of the anti-GBM disease as there were no other exposures or serologic findings suggesting other causes. INTERVENTIONS: She was treated with corticosteroids, cyclophosphamide, and plasmapheresis. She required hemodialysis for acute renal failure. OUTCOMES: Despite treatment, the patient's renal function did not recover. She remained dialysis-dependent and anti-GBM antibody titers remained elevated 6 months after presentation. TEACHING POINTS: Anti-GBM disease is a life-altering adverse event that can be associated with alemtuzumab. Our case highlights the limitations of monitoring urinalyses as a trigger for anti-GBM antibody testing in patients who have received alemtuzumab and have baseline abnormal urinalyses; such patients may require further protocolized anti-GBM antibody testing, although the optimal frequency of such antibody screening remains unclear.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".