A retrospective claims analysis: Compliance and discontinuation rates among Canadian patients with multiple sclerosis treated with disease-modifying therapies
Bibliographic record
Abstract
BACKGROUND: Compliance to disease modifying therapy (DMT) is associated with a reduced risk of relapse, lower healthcare resource utilization, and improved health-related quality of life in patients with multiple sclerosis (MS). Our objective was to assess the compliance and discontinuation rates of fingolimod relative to other oral, injectable, and infusible DMTs available on the market at the time of the study in Canada in patients with relapsing-remitting MS (RRMS). METHODS AND FINDINGS: We conducted a retrospective claims analysis. Patients with RRMS with ≥ 1 prescription for each DMT were included. Compliance (medication possession ratio of ≥ 80%) and discontinuation (gap > 30 days from the end of the index prescription) were calculated at the 6-, 12- and 24-month time points. Compliance with fingolimod at the 6-, 12- and 24-month time points was 75%, 75% and 70%, respectively; compared with DMF [70% (P < 0.001), 68% (P < 0.001), and 56% (P < 0.001), respectively], and BRACE [53% (P < 0.001), 47% (P < 0.001), and 35% (P < 0.001), respectively]. Compliance with fingolimod was comparable to teriflunomide at each time point, but was higher compared to natalizumab [70% versus 57% (P < 0.001)] at the 24-month time point. At the 6-, 12- and 24-month time points, patients on fingolimod had the lowest discontinuation rate (26%, 24%, and 29%, respectively) compared to BRACE [49% (P < 0.001), 44% (P < 0.001), and 57% (P < 0.001)], respectively], and natalizumab [33% (P < 0.001), 29% (P < 0.001), and 45% (P < 0.001), respectively], and was similar to teriflunomide (26%, 25%, and 31%, respectively). CONCLUSIONS: The compliance rate in fingolimod treated patients at the 24 month time point was higher than that observed in natalizumab treated patients. The discontinuation rate was lower with fingolimod compared to other DMTs at all time points but was similar to teriflunomide.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".