How effective are disease-modifying drugs in delaying progression in relapsing-onset MS?
Bibliographic record
Abstract
OBJECTIVE: Our objective was to estimate the effectiveness of disease-modifying drugs (DMDs) in delaying multiple sclerosis (MS) disability progression in relapsing-onset (R-onset) definite MS patients under "real-world" conditions. METHODS: Treatment effect size, for DMDs as a class, was estimated in absolute terms and relative to MS natural history. A basic model estimated annual Expanded Disability Status Scale (EDSS) change before and after treatment. An expanded model estimated annual EDSS change in pretreatment years, treatment years on first drug, treatment years after drugs were switched, and in years after treatment stopped. Models were populated with 1980 through 2004 clinical data, including 1988 through 2004 data for all Nova Scotians treated with DMDs. Estimates were made for relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), and R-onset groups. RESULTS: Estimated pretreatment annual EDSS increases were approximately 0.10 of one EDSS point for the RRMS group, 0.31 for the SPMS group, and 0.16 for the R-onset group. Estimates of EDSS increase avoided per treatment year on the first drug were significant for the RRMS group (-0.103, 0.000), the SPMS group (-0.065, 0.011), and the R-onset group (-0.162, 0.000); relative effect size estimates were 112%, 21%, and 105%. Estimated EDSS progression was faster in years after drug switches and treatment stops. CONCLUSIONS: Our estimates of disease-modifying drug (DMD) relative treatment effect size, in the context of "real-world" clinical practice, are similar to DMD treatment efficacy estimates in pivotal trials, though our findings attained statistical significance. DMDs, as a class, are effective in delaying Expanded Disability Status Scale progression in patients with relapsing-onset definite multiple sclerosis (MS) (90%), although effectiveness is much better for relapsing-remitting MS than for secondary progressive MS groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".