‘Clinically definite benign multiple sclerosis’, an unwarranted conceptual hodgepodge: evidence from a 30-year observational study
Bibliographic record
Abstract
BACKGROUND: Benign multiple sclerosis (BMS) is a controversial concept which is still debated. However identification of this kind of patients is crucial to prevent them from unnecessary exposure to aggressive and/or long term medical treatments. OBJECTIVES: To assess two definitions of 'clinically definite benign multiple sclerosis' (CDBMS) using long-term follow-up data, and to look for prognostic factors of CDBMS. METHODS: In 874 patients with definite relapsing-remitting MS, followed up for at least 10 years, disability was assessed using the Disability Status Scale (DSS). CDBMS was defined by either DSS score≤2 (CDBMS1 group) or DSS score≤ 3 (CDBMS2 group) at 10 years. We estimated the proportion of patients who were still benign at 20 and 30 years after clinical onset. RESULTS: CDBMS frequency estimates were 57.7% and 73.9% when using CDBMS1 and CDBMS2 definitions, respectively. In the CDBMS1 group, only 41.7% (105/252) of cases were still benign 10 years later, and 41.1% (23/56) after an additional decade, while there were 53.8% (162/301) and 59.5% (44/74) respectively in the CDBMS2 group. CONCLUSIONS: This 30-year observational study, which is one of the largest published series, indicates that favourable 10-year disability scores of DSS 2 or 3 fail to ensure a long-term benign course of multiple sclerosis. After every decade almost half of the CDBMS were no longer benign. CDBMS, as currently defined, is an unwarranted conceptual hodgepodge. Other criteria using new biomarkers (genetic, biologic or MRI) should be found to detect benign cases of MS.
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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 | 0.023 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".