Saudi Consensus Recommendations on the Management of Multiple Sclerosis: Disease-Modifying Therapies and Management of Relapses
Bibliographic record
Abstract
For the past 10 years, disease-modifying therapy (DMT) options for multiple sclerosis (MS) have grown remarkably where DMTs have been shown to reduce the risk of MS relapses. MS patients are advised to begin treatment with a DMT shortly after diagnosis to limit the possibility of disease progression over time. While patients with radiologically isolated syndrome do not require pharmacologic treatment, high-risk patients with clinically isolated syndrome are advised to start DMTs. This article provides evidence-based recommendations for DMT use in MS management, helping healthcare practitioners advise patients on treatment decisions. We aim to provide recommendations for the management of acute MS relapses. The recommendations herein were developed following the gathering of a panel of experts after evaluating international guidelines, and the latest evidence was collected through a comprehensive literature review.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".