Expert opinion on COVID-19 vaccination and the use of cladribine tablets in clinical practice
Bibliographic record
Abstract
BACKGROUND: Gaps in current evidence and guidance leave clinicians with unanswered questions on the use of cladribine tablets for the treatment of multiple sclerosis (MS) in the era of the COVID-19 pandemic, in particular relating to COVID-19 vaccination. OBJECTIVE: We describe a consensus-based program led by international MS experts with the aim of supplementing current guidelines and treatment labels by providing timely recommendations relating to COVID-19 vaccination and the use of cladribine tablets in clinical practice. METHODS: A steering committee (SC) of 10 international MS experts identified 7 clinical questions to answer concerning the use of cladribine tablets and COVID-19 vaccination, which addressed issues relating to patient selection, timing and efficacy, and safety. Clinical recommendations to address each question were drafted using available evidence combined with expert opinion from the SC. An extended faculty of 28 MS experts, representing 19 countries, in addition to the 10 SC members, voted on the recommendations. Consensus on recommendations was achieved when ⩾75% of respondents expressed an agreement score of 7-9, on a 9-point scale. RESULTS: Consensus was achieved on all 13 recommendations. Clinical recommendations are provided on whether all patients with MS receiving cladribine tablets should be vaccinated against COVID-19, and whether they should be prioritized; the timing of vaccination around dosing of cladribine tablets (i.e. before and after a treatment course); and the safety of COVID-19 vaccination for these patients. CONCLUSION: These expert recommendations provide timely guidance on COVID-19 vaccination in patients receiving cladribine tablets, which is relevant to everyday clinical practice.
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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 | 0.132 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".