Safety of Biologic and Nonbiologic Disease-modifying Antirheumatic Drug Therapy in Veterans with Rheumatoid Arthritis and Hepatitis C Virus Infection
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of disease-modifying antirheumatic drug (DMARD) therapy on hepatotoxicity among patients with rheumatoid arthritis (RA) and hepatitis C virus (HCV) infection. METHODS: We identified biologic and nonbiologic treatment episodes of patients with RA using the 1997-2011 national data from the US Veterans Health Administration. Eligible episodes had HCV infection (defined by detectable HCV RNA) and subsequently initiated a new biologic or nonbiologic DMARD. Cohort entry required a baseline alanine aminotransferase (ALT) < 66 IU/l and quantifiable HCV RNA within 90 days prior to starting biologic/DMARD therapy. The primary outcome of interest was hepatotoxicity, defined as ALT elevation ≥ 100 IU/l or increase in HCV RNA of 1 log or more, and was examined within the first year of biologic/DMARD use. Results were reported as the cumulative incidence of treatment episodes achieving predefined hepatotoxicity at 3, 6, and 12 months after biologic/DMARD initiation. RESULTS: RA patients with HCV (n = 748) were identified and contributed 1097 biologic/DMARD treatment episodes. Overall, ALT elevations were uncommon, with 37 (3.4%) hepatotoxicity events occurring within 12 months. Treatment episodes with biologic DMARD demonstrated more frequency of hepatotoxicity than did nonbiologic DMARD (4.8% vs 2.3%, p = 0.03). Among treatment episodes involving hepatotoxicity events, the majority occurred within 6 months of DMARD initiation (29/37, 78%). CONCLUSION: In US veterans with HCV and RA receiving biologic and nonbiologic DMARD, the frequency of hepatotoxicity (ALT ≥ 100 IU/l) was low, with a higher frequency observed in treatment episodes with current biologic use.
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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 | 0.002 | 0.006 |
| 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".