Timing of high-efficacy therapy in relapsing-remitting multiple sclerosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Immunotherapy initiated early after first presentation of relapsing-remitting multiple sclerosis is associated with improved long-term outcomes. One can therefore speculate that early initiation of highly effective immunotherapies, with an average efficacy that is superior to the typical first-line therapies, could further improve relapse and disability outcomes. However, the most common treatment strategy is to commence first-line therapies, followed by treatment escalation in patients who continue to experience on-treatment disease activity. While this monitoring approach is logical, the current lack of effective regenerative or remyelinating therapies behoves us to consider high-efficacy treatment strategies from disease onset (including induction therapy) in order to prevent irreversible disability. OBJECTIVE: In this systematic review, we evaluate the effect of high-efficacy immunotherapies at different stages of MS. METHODS: A systematic review of literature reporting outcomes of treatment with fingolimod, natalizumab or alemtuzumab at different stages of MS was carried out. RESULTS AND CONCLUSIONS: Twelve publications reporting relevant information were included in the systematic review. The literature suggests that treatment with high-efficacy immunotherapies is more potent in suppressing relapse activity when initiated early vs. with a delay after the MS diagnosis. The evidence reported for disability and MRI outcomes is inconclusive.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".