Treatment options for patients with multiple sclerosis who have a suboptimal response to interferon‐<i>β</i> therapy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although the first-line disease-modifying therapies (DMTs) interferon beta and glatiramer acetate have a favourable benefit-to-risk profile, they are only partially effective for treating relapsing-remitting multiple sclerosis (RRMS). The optimization of treatment in patients who do not show a maximum response to first-line therapy is critical for achieving the best long-term outcomes. Treatment strategies for patients with a suboptimal response include switching to another first-line DMT or a second-line DMT. Natalizumab and fingolimod are approved for RRMS with high disease activity in the European Union and Canada. METHODS: A comprehensive literature search for articles published between 1990 and April 2012 was undertaken. RESULTS: This review discusses key clinical and safety data for fingolimod and natalizumab, particularly in the patient subgroups for whom these treatments are approved. Benefit-to-risk profiles, including first-dose cardiovascular effects associated with fingolimod and the risk of progressive multifocal encephalopathy with natalizumab, are discussed. CONCLUSION: A descriptive comparison of fingolimod and natalizumab is provided in the context of the decision-making process of how and when to switch patients who have a suboptimal response to first-line therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".