Country, Sex, EDSS Change and Therapy Choice Independently Predict Treatment Discontinuation in Multiple Sclerosis and Clinically Isolated Syndrome
Bibliographic record
Abstract
OBJECTIVES: We conducted a prospective study, MSBASIS, to assess factors leading to first treatment discontinuation in patients with a clinically isolated syndrome (CIS) and early relapsing-remitting multiple sclerosis (RRMS). METHODS: The MSBASIS Study, conducted by MSBase Study Group members, enrols patients seen from CIS onset, reporting baseline demographics, cerebral magnetic resonance imaging (MRI) features and Expanded Disability Status Scale (EDSS) scores. Follow-up visits report relapses, EDSS scores, and the start and end dates of MS-specific therapies. We performed a multivariable survival analysis to determine factors within this dataset that predict first treatment discontinuation. RESULTS: A total of 2314 CIS patients from 44 centres were followed for a median of 2.7 years, during which time 1247 commenced immunomodulatory drug (IMD) treatment. Ninety percent initiated IMD after a diagnosis of MS was confirmed, and 10% while still in CIS status. Over 40% of these patients stopped their first IMD during the observation period. Females were more likely to cease medication than males (HR 1.36, p = 0.003). Patients treated in Australia were twice as likely to cease their first IMD than patients treated in Spain (HR 1.98, p = 0.001). Increasing EDSS was associated with higher rate of IMD cessation (HR 1.21 per EDSS unit, p<0.001), and intramuscular interferon-β-1a (HR 1.38, p = 0.028) and subcutaneous interferon-β-1a (HR 1.45, p = 0.012) had higher rates of discontinuation than glatiramer acetate, although this varied widely in different countries. Onset cerebral MRI features, age, time to treatment initiation or relapse on treatment were not associated with IMD cessation. CONCLUSION: In this multivariable survival analysis, female sex, country of residence, EDSS change and IMD choice independently predicted time to first IMD cessation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".