Vaccination and multiple sclerosis in the era of the COVID-19 pandemic
Bibliographic record
Abstract
In the last century, several pandemics were caused by various influenza virus subtypes, with the Spanish influenza of 1918–1920 being the most severe.1 Coronaviruses were first described in humans in the 1960s, and since then seven human pathogenic coronaviruses have been described.2 The first severe outbreak was in 2002 with the severe acute respiratory syndrome coronavirus (SARS-CoV), followed by the Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak in 2012.3 The current COVID-19 pandemic, which broke out in Wuhan, China, in late 2019, is caused by the highly transmissible RNA virus SARS-CoV-2.4 As of 11 June 2021, 174 439 909 COVID-19 cases and 3 768 987 deaths have been reported worldwide, according to the WHO Dashboard.5 Infectious diseases significantly contribute to morbidity in patients with multiple sclerosis (MS), since the risk of suffering from an infection is increased by MS per se and may be further enhanced by disease-modifying therapies (DMTs) and frequent comorbidities.6–9 As a result, infectious diseases may heighten the risk of disease progression in MS and result in reduced life expectancy.10–12 The COVID-19 pandemic, in particular, presents new challenges for patients with MS. Since the approval of messenger RNA (mRNA) and non-replicating vector coronavirus vaccines by the European Medicines Agency (EMA), the UK Medicines and Healthcare products Regulatory Agency (MHRA), the US Food and Drug Administration (FDA), Health Canada (HC) and the Australian Therapeutic Goods Administration (TGA), neurologists are facing an unprecedented demand for information on vaccination in MS, particularly in the context of DMTs.13–17 This review aims to provide a clinical practice-oriented approach to SARS-CoV-2 vaccination in MS in the era of the COVID-19 pandemic.18–21 COVID-19 is a disease caused by the highly contagious RNA virus SARS-CoV-2. It shares a 79% genetic match with SARS-CoV and a 50% genetic match …
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".