Persistence to disease-modifying therapies for multiple sclerosis in a Canadian cohort
Bibliographic record
Abstract
PURPOSE: To examine the long-term persistence to the first-line injectable disease-modifying therapies (DMTs) for multiple sclerosis (MS) and to identify the factors associated with nonpersistence. PATIENTS AND METHODS: We used population-based administrative data from Manitoba, Canada. All adult subjects who were diagnosed with MS and dispensed a first-line injectable DMT (beta-interferon-1b, beta-interferon-1a, and glatiramer acetate) between 1996 and 2011 and had a minimum of 1 year of follow-up were included. The primary outcome was the median time to discontinuation of any DMT. The associations between potential predictors and persistence were estimated using multivariable Cox-proportional hazard models. RESULTS: Overall, 721 subjects were followed for a median of 7.8 years (interquartile range 6.1). The median time to discontinuation of all first-line DMTs was 4.2 years (25th and 75th percentile: 1.7, 10.6 years). Of the 451 (62.6%) subjects who discontinued their DMT during the study period, 259 (57.4%) eventually resumed or restarted a DMT. Subjects who were younger when starting a DMT, had prior MS-related hospitalizations, were more recently diagnosed with MS, or had a greater lag time between their MS diagnosis and DMT initiation were more likely to discontinue therapy. CONCLUSION: Over half of the individuals receiving a DMT for MS in Manitoba remained on therapy for at least 4 years. DMT discontinuation occurred in 60% of the cohort, but most restarted a DMT within 1 year. While not all of the factors identified with discontinuing DMT are modifiable, they may help practitioners enhance MS care by identifying individuals who may be at particular risk for DMT discontinuation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".