High-dose frequency beta-interferons increase the risk of liver test abnormalities in multiple sclerosis: a longitudinal study
Bibliographic record
Abstract
BACKGROUND: Post-marketing studies and case reports have linked beta-interferon (IFNβ) treatment with liver enzyme abnormalities and liver injuries in patients with multiple sclerosis (MS). Few predictors of risk exist. OBJECTIVE: We investigated the effect of IFNβ and other patient characteristics on levels of the liver enzyme, alanine aminotransferase (ALT). METHOD: Repeated ALT test results were reviewed retrospectively for 1064 MS patients prescribed an IFNβ as their first immunomodulatory drug. Liver enzyme abnormality was defined as an ALT elevation twice the upper limit of normal (≥ 2 ULN). The Generalized Estimating Equation (GEE) was used to analyze the effect of age (≤ 35, >35-40, >40-45, >45 years), gender, disease duration, IFNβ product, and duration of treatment (≤ 5, >5-15, >15-40, >40 months) on de novo liver enzyme abnormality. RESULTS: Over a mean treatment period of 38.7 months (SD=34.9), 12.4% (95/766) of MS patients developed de novo liver enzyme abnormality. Multivariable GEE results showed a dose frequency response effect of IFNβs on liver enzyme abnormality: OR=3.8(95% CI: 1.6-9.2) for IFNβ-1a 44 µg SC, and OR=3.4 (95% CI: 1.5-7.9) for IFNβ-1b 250 µg SC compared with the lower frequency IFNβ-1a 30 µg IM. Younger age (≤ 40 years), male gender, and ≤ 15 months of IFNβ exposure were also independent predictors. CONCLUSION: A dose frequency response effect was observed, with high-frequency IFNβs having the greatest risk. The first 15 months of treatment, men, and younger patients were also associated with elevated risk. Regular ALT monitoring in MS patients appears prudent; long-term consequences of ALT elevations should be further investigated.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".