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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".