Evaluating the safety of β-interferons in MS
Bibliographic record
Abstract
Objective:To examine the association between interferon-β (IFN-β) and potential adverse events using population-based health administrative data in British Columbia, Canada. Methods:Patients with relapsing-remitting multiple sclerosis (RRMS) who were registered at a British Columbia Multiple Sclerosis Clinic (1995–2004) were eligible for inclusion and were followed up until death, absence from British Columbia, exposure to a non–IFN-β disease-modifying drug, or December 31, 2008. Incidence rates were estimated for each potential adverse event (selected a priori and defined with ICD-9/10 diagnosis codes from physician and hospital claims). A nested case-control study was conducted to assess the odds of previous IFN-β exposure for each potential adverse event with at least 30 cases. Cases were matched by age (±5 years), sex, and year of cohort entry, with up to 20 randomly selected (by incidence density sampling) controls. Odds ratios (ORs) with 95% confidence intervals (95% CIs) were estimated with conditional logistic regression adjusted for age at cohort entry. Results:Of the 2,485 eligible patients, 77.9% were women, and 1,031 were treated with IFN-β during follow-up. From the incidence analyses, 27 of the 47 potential adverse events had at least 30 cases. Patients with incident stroke (ORadj 1.83, 95% CI 1.16–2.89), migraine (ORadj 1.55, 95% CI 1.18–2.04), depression (ORadj 1.33, 95% CI 1.13–1.56), and hematologic abnormalities (ORadj 1.32, 95% CI 1.01–1.72) were more likely to have previous exposure to IFN-β than controls. Conclusions:Among patients with RRMS, IFN-β was associated with a 1.8- and 1.6-fold increase in the risk of stroke and migraine and 1.3-fold increases in depression and hematologic abnormalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".