Approach to Managing the Initial Presentation of Multiple Sclerosis
Bibliographic record
Abstract
Background and Objectives: Available disease-modifying therapies (DMTs) for multiple sclerosis (MS) are rapidly expanding; although escalation approaches aim to balance safety and efficacy, emerging evidence suggests superior outcomes for people with MS who are exposed to early high-efficacy therapies. We aimed to explore practice differences in prevailing management strategies for relapsing-remitting MS. Methods: . Questions pertained to a case of a 37-year-old woman presenting with optic neuritis. Respondents were asked to indicate their initial investigations, relapse management strategy, choice of disease-modifying therapy, and plan for follow-up imaging (contrast/noncontrast). Survey responses were stratified by key demographic variables along with 95% confidence intervals (95% CIs). Results: We received 153 responses from 42 countries; 32.3% responders identified as MS specialists. There was a strong preference for intravenous delivery of high-dose corticosteroids (87.7%, 95% CI 80.7-92.5), and most of the responders (61.3%, 95% CI 52.6-69.4) indicated they would treat a nondisabling (mild sensory) MS relapse. When asked to select a single initial DMT, 56.6% (95% CI 47.6-65.1) selected a high-efficacy therapy (67.5% MS specialists vs 53.7% non-MS specialists). The most selected agents overall were fingolimod (14.7%), natalizumab (15.5%), and dimethyl fumarate (20.9%). Two-thirds of respondents indicated they would request contrast-enhanced surveillance MRI. Discussion: Although there is a slight preference for initiating high-efficacy DMT at the time of initial MS diagnosis, opinions regarding the most appropriate treatment paradigm remain divided.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".