Anti‐inflammatory disease‐modifying treatment and disability progression in primary progressive multiple sclerosis: a cohort study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Treatment options in primary progressive multiple sclerosis (PPMS) are scarce and, with the exception of ocrelizumab, anti-inflammatory agents have failed to show efficacy in ameliorating disability progression. The aim of this study was to investigate a potential effect of anti-inflammatory disease-modifying treatment on disability outcomes in PPMS. METHODS: Using MSBase, a large, international, observational database, we identified patients with PPMS who were either never treated or treated with a disease-modifying agent. Propensity score matching was used to select subpopulations with similar baseline characteristics. Expanded Disability Status Scale (EDSS) outcomes were compared with an intention-to-treat and an as-treated approach in paired, pairwise-censored analyses. RESULTS: Of the 1284 included patients, 533 were matched (treated, n = 195; untreated n = 338). Median on-study pairwise-censored follow-up was 3.4 years (quartiles 1.2-5.5). No difference in the hazard of experiencing 3-month confirmed EDSS progression events was observed between the groups [hazard ratio (HR), 1.0; 95% confidence interval (CI), 0.6-1.7, P = 0.87]. We did not find significant differences in the hazards of confirmed EDSS improvement (HR, 1.0; 95% CI, 0.6-1.6, P = 0.91) or reaching a confirmed EDSS step ≥7 (HR, 1.1; 95% CI, 0.7-1.6, P = 0.69). CONCLUSION: Our pooled analysis of disease-modifying agents suggests that these therapies have no substantial effect on short- to medium-term disability outcomes in PPMS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".