A Review of Azathioprine-Associated Hepatotoxicity and Myelosuppression in Myasthenia Gravis
Bibliographic record
Abstract
OBJECTIVES: Myasthenia gravis (MG) is an autoimmune disorder in which antibodies interfere with neuromuscular transmission. Azathioprine (AZA) is an immunosuppressant frequently used for treatment of various autoimmune conditions, including MG. The literature suggests that the rates of AZA-associated hepatotoxicity and myelosuppression in MG are highly variable. Published studies have not formally analyzed their pattern, severity, timing, and/or recovery. We assessed the prevalence, pattern and timing of AZA associated toxicity in a large group of MG patients. METHODS: We identified 113 patients with MG with AZA-associated toxicity among 571 managed with this immunosuppressant. The timing of when toxicities occurred as well as pattern of laboratory abnormalities was assessed. RESULTS: The overall prevalence of hepatotoxicity and myelosuppression was 15.2% and 9.1%, respectively. The most common pattern of hepatotoxicity seen was gamma-glutamyl transpeptidase (GGT) enzyme elevation in 67.8% of patients. Of note, 21.2% of patients with myelosuppression had normocytic anemia, 17.3% had pancytopenia, and another 17.3% developed macrocytic anemia. CONCLUSIONS: AZA-associated hepatotoxicity and myelosuppression in MG are not uncommon and may be underrecognized depending on the timing, frequency, and specific tests ordered for blood work monitoring.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".