Anti-inflammatory disease-modifying treatment and short-term disability progression in SPMS
Bibliographic record
Abstract
Objective: To investigate the effect of disease-modifying treatment on short-term disability outcomes in secondary progressive multiple sclerosis (SPMS). Methods: Using MSBase, an international cohort study, we previously validated a highly accurate definition of SPMS. Here, we identified patients in MSBase who were either untreated or treated with a disease-modifying drug when meeting this definition. Propensity score matching was used to select subpopulations with comparable baseline characteristics. Disability outcomes were compared in paired, pairwise-censored analyses adjusted for treatment persistence, visit density, and relapse rates. Results: Of the 2,381 included patients, 1,378 patients were matchable (treated n = 689, untreated n = 689). Median pairwise-censored follow-up was 2.1 years (quartiles 1.2–3.8 years). No difference in the risk of 6-month sustained disability progression was observed between the groups (hazard ratio [HR] 0.9, 95% confidence interval [CI] 0.7–1.1, p = 0.27). We also did not find differences in any of the secondary endpoints: risk of reaching Expanded Disability Status Scale (EDSS) score ≥7 (HR 0.6, 95% CI 0.4–1.1, p = 0.10), sustained disability reduction (HR 1.0, 95% CI 0.8–1.3, p = 0.79), or change in disability burden (area under the EDSS-time curve, β = −0.05, p = 0.09). Secondary and sensitivity analyses confirmed the results. Conclusions: Our pooled analysis of the currently available disease-modifying agents used after conversion to SPMS suggests that, on average, these therapies have no substantial effect on relapse-unrelated disability outcomes measured by the EDSS up to 4 years. Classification of evidence: This study provides Class IV evidence that for patients with SPMS, disease-modifying treatment has no beneficial effect on short-term disability progression.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".