Outcomes of COVID-19 among patients treated with subcutaneous interferon beta-1a for multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: In accordance with expert guidance, patients have typically continued to receive treatment with subcutaneous interferon beta-1a (sc IFN β-1a) for relapsing multiple sclerosis (MS) during the COVID-19 pandemic. METHODS: We provide a summary of outcomes among sc IFN β-1a-treated patients with adverse events related to confirmed or suspected COVID-19, as reported to the Merck Global Patient Safety Database (as of 2 February 2021). Serious COVID-19-related adverse events (as classified by the reporting clinician) included those leading to hospitalization, admission to intensive care, or death. Outcomes were classified per usual pharmacovigilance practice. RESULTS: The evaluable cohort comprised 603 patients of median age 43 (range, 13-84) years and 75.1% were female. COVID-19 was experienced at a median of 33.0 (range, 0-321.8) months after start of treatment with sc IFN β-1a. A total of 136 (22.6%) patients experienced serious COVID-19 events, including 59 hospitalizations (4 patients admitted to intensive care) and 5 deaths (fatality rate, 0.8%). Regarding non-fatal outcomes, 47.8% of patients (289/603) with COVID-19 adverse events were recovered or recovering at time of analysis; similar findings were apparent for the serious and hospitalized cohorts. CONCLUSION: Findings of this analysis from the Merck Global Patient Safety Database suggest that, compared with available statistics for the general population and those with MS, patients receiving sc IFN β-1a for treatment of relapsing MS have relatively low rates of serious disease and/or severe outcomes with COVID-19.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".