Characteristics associated with drug-induced liver injury from interferon beta in multiple sclerosis patients
Bibliographic record
Abstract
OBJECTIVE: To identify and characterize drug-induced liver injury (DILI) associated with IFN-β in multiple sclerosis (MS) using recommended criteria. METHODS: This retrospective, mixed methods design included a cohort of IFN-β exposed MS patients from British Columbia (BC), Canada and a series of DILI cases from other Canadian provinces and two adverse drug reaction (ADR) networks (USA and Sweden). Associations between sex, age and IFN-β product, and DILI were explored in BC cohort using Cox proportional hazard analyses. Characteristics, including the time to DILI, were compared between sites. RESULTS: In BC, 18/942 (1.9%) of IFN-β exposed MS patients met criteria for DILI, with a trend toward an increased risk for women and those exposed to IFN-β-1a SC (44 mcg 3 × weekly) (adjusted Hazard Ratios: 3.15;95% CI:0.72 - 13.72, p = 0.13 and 6.26;95%CI:0.78 - 50.39, p = 0.08, respectively). Twenty-four additional cases were identified from other sites; the median time to DILI was comparable between BC and other Canadian cases (105 and 90 days, respectively), but longer for the ADR network cases (590 days, p = 0.006). CONCLUSIONS: Approximately 1 in 50 IFN-β exposed patients developed DILI in BC, Canada. Identification of DILI cases from diverse sources highlighted that this reaction occurs even after years of exposure.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".