A plain language summary of the impact of vaccines against flu and chickenpox in people with multiple sclerosis treated with cladribine tablets
Bibliographic record
Abstract
A plain language summary of the impact of vaccines against flu and chickenpox in people with multiple sclerosis treated with cladribine tabletsWhat is this summary about?This is a summary of an article originally published in the Multiple Sclerosis Journal.Cladribine tablets (MAVENCLAD®) are an oral (taken by mouth) medication, approved for the treatment of people with relapsing forms of multiple sclerosis (MS, with episodes of new or worsening symptoms).They are administered for a maximum of 10 days per year, over a period of 2 years.Cladribine tablets work by temporarily reducing the number of lymphocytes, which are immune cells that help to fight off infections.Because of this, people with MS (also called PwMS) may have concerns about the effect of cladribine tablets on vaccines, as these work via immune cells to build protection against infection. What happened in the MAGNIFY-MS study?A study called MAGNIFY-MS investigated how long it takes for cladribine tablets to begin to work in people with a type of MS called highly active relapsing MS.During the study, some participants received their usual vaccinations against flu (influenza) and against the chickenpox virus (also called varicella zoster virus) as part of their routine medical care.The MAGNIFY-MS study gave the researchers an opportunity to look at how cladribine tablets affect the way the flu and chickenpox virus vaccines work in the body.What were the results?Cladribine tablets do not affect how well the body responds to flu and chickenpox vaccines. What do the results mean?PwMS taking cladribine tablets who are vaccinated against chickenpox, flu or both can be protected against these diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.261 | 0.027 |
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".