Assessing the risk of multiple sclerosis disease-modifying therapies
Bibliographic record
Abstract
Introduction: The number of immunomodulatory options approved for multiple sclerosis has increased over the past years, resulting in a better control of the disease. Depending on disease activity, neurologists can now propose treatments with different levels of efficacy, from injectable and oral treatments with modest efficacy, to highly active immunosuppressants. Nevertheless, this gain in efficacy has come with an increase in the global burden of treatment-related adverse events.Areas covered: The authors have reviewed extensively the existing literature to gain insight into the adverse events associated with disease modifying therapies, so as to help neurologists choose the right treatment for their patients. The authors classified and summarized the adverse events based on frequency, severity and relevance.Expert opinion: As the number and diversity of adverse events is expected to increase, careful surveillance of patients under treatment will be even more important. The characteristics of the MS population, i.e. mainly young women of childbearing age, who will remain treated for decades, and the need for serial administration of distinct treatments with different mechanisms of action highlights the importance of a comprehensive risk-benefit assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".