Disease progression among multiple sclerosis patients before and during a disease-modifying drug program: a longitudinal population-based evaluation
Bibliographic record
Abstract
Randomized controlled trials have demonstrated the efficacy of disease-modifying drugs (DMDs) in persons with relapsing-remitting multiple sclerosis (MS) and secondary progressive MS with superimposed relapses. However, these brief studies of selected patients have focused mainly on reducing attacks and must be complemented by evaluations in 'realworld' clinical settings to establish the effectiveness of DMD programs in slowing disease progression and to inform health policy and program decision-making. We assessed the effectiveness of DMDs as administered in a comprehensive publicly funded drug insurance program that provides DMDs to a geographically defined population of MS patients who meet specific eligibility criteria. Data from 1752 MS patients (10,312 assessments) seen between 1980 and 2004 at a regional MS Clinic serving the entire population of Nova Scotia, Canada were analysed. Using survival methods we observed a statistically significant reduction in disease progression to specific Expanded Disability Status Scale endpoints following the introduction of this program. Subgroup analyses of patients eligible for treatment using hierarchical linear regression methods also suggested that disease progression was slowed in patients treated with the first DMD prescribed. These findings provide evidence supporting DMD program effectiveness that can be used to inform the broader implementation of such programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".