Timing of high-efficacy therapy in relapsing-remitting multiple sclerosis: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it