Long-Term Persistence with Injectable Therapy in Relapsing-Remitting Multiple Sclerosis: An 18-Year Observational Cohort Study
Bibliographic record
Abstract
Disease modifying therapies (DMTs) reduce the frequency of relapses and accumulation of disability in multiple sclerosis (MS). Long-term persistence with treatment is important to optimize treatment benefit. This long-term, cohort study was conducted at the Calgary MS Clinic. All consenting adults with relapsing-remitting MS who started either glatiramer acetate (GA) or interferon-β 1a/1b (IFN-β) between January 1st, 1996 and July 1st, 2011 were included. Follow-up continued to February 1st, 2014. Time-to-discontinuation of the initial and subsequently-prescribed DMTs (switches) was analysed using Kaplan-Meier survival analyses. Group differences were compared using log-rank tests and multivariable Cox regression models. Analysis included 1471 participants; 906 were initially prescribed GA and 565 were initially prescribed IFN-β. Follow-up information was available for 87%; 29 (2%) were lost to follow-up and 160 (11%) moved from Southern Alberta while still using DMT. Median time-to-discontinuation of all injectable DMTs was 11.1 years. Participants with greater disability at treatment initiation, those who started treatment before age 30, and those who started between 2006 and 2011 were more likely to discontinue use of all injectable DMTs. Median time-to-discontinuation of the initial DMT was 8.6 years. Those initially prescribed GA remained on treatment longer. Of 610 participants who discontinued injectable DMT, 331 (54%) started an oral DMT, or a second-line DMT, or resumed injectable DMT after 90 days. Persistence with injectable DMTs was high in this long-term population-based study. Most participants who discontinued injectable DMT did not remain untreated. Further research is required to understand treatment outcomes and outcomes after stopping DMT.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".