COVID-19 Infection in Fingolimod- or Siponimod-Treated Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A descriptive analysis of COVID-19 infection in patients with multiple sclerosis (MS) receiving fingolimod or siponimod. METHODS: We reviewed the cases of COVID-19 from postmarketing or ongoing clinical trials reported to Novartis through December 27, 2020. RESULTS: As of December 27, 2020, 283 cases had been reported in fingolimod-treated patients. The mean age was 44 years (from n = 224; range 11-69 years), and 190 were women. Of 161 cases with available information, 138 were asymptomatic (6), mild (100), or moderate (32); 50 cases required hospitalization. At the last follow-up, 140 patients were reported as recovered/recovering, condition was unchanged in 22, and deteriorated in 3 patients; 4 patients had a fatal outcome. Information was not available for 114 patients. Of the 54 cases of COVID-19 reported in siponimod-treated patients, 45 were from the postmarketing setting and 9 from an ongoing open-label clinical trial. The mean age was 54 years (from n = 45; range 31-70), and 30 were women. Of 28 cases with available information, 24 were asymptomatic (2), mild (17), or moderate (5); 9 cases required hospitalization. At the last follow-up, 27 patients were reported as recovered/recovering, condition remained unchanged for 1, and 3 patients had a fatal outcome. Information was not available for 23 patients. DISCUSSION: Based on a review of available information, the risk of more severe COVID-19 in patients receiving fingolimod or siponimod seems to be similar to that reported in the general population and the MS population with COVID-19. However, limitations of spontaneous reporting, especially missing data, should be considered in the interpretation of these observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".