Effect of adherence to the first-generation injectable immunomodulatory drugs on disability accumulation in multiple sclerosis: a longitudinal cohort study
Bibliographic record
Abstract
OBJECTIVE: To examine the association between optimal adherence to the first-generation injectable immunomodulatory drugs (IMDs) for multiple sclerosis (MS) and subsequent disability accumulation. METHODS: We accessed prospectively collected linked clinical and administrative health data from British Columbia, Canada. Subjects with MS treated with a first-generation injectable IMD at an MS clinic (1996-2004) were followed until their last clinic visit before 2009. Adherence was estimated using the proportion of days covered (PDC). The primary outcome was disability accumulation, defined as an increase in the Expanded Disability Status Scale (EDSS) score as recorded during each year of follow-up. Generalised estimating equation models, adjusted for baseline sex, age, EDSS and time between scores, were used to measure associations between optimal adherence (≥80% PDC) during the first year of treatment and subsequent disability accumulation. The relationship between early IMD adherence and the secondary outcome, time to sustained EDSS 6, was examined using Cox proportional hazards regression. RESULTS: Among 801 subjects, 598 (74.7%) had optimal adherence over the first year of IMD treatment and 487 (39.0%) demonstrated one or more instances of disability accumulation. Early optimal adherence was not associated with disability accumulation (adjusted OR 0.94; 95% CI 0.78 to 1.15), nor with time to sustained EDSS 6 (adjusted HR 0.91; 95% CI 0.57 to 1.44). CONCLUSION: Almost three-quarters of subjects with MS had optimal early adherence to their first-line injectable IMD. There was no evidence that this was associated with disability accumulation in the following years.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".