The low adherence and disability outcomes of disease-modifying drugs in Multiple Sclerosis in Saskatchewan, a cohort study, 1997-2014
Bibliographic record
Abstract
Background: The beneficial effects of the injected disease-modifying drugs (DMDs) in relapsing-remitting Multiple Sclerosis have been previously reported. However the results related to disability outcomes and the reduction of disease progression in the pivotal trials and few longer studies are variable and inconclusive. Objectives: To determine the utilization and the disability outcomes of the DMDs on relapsing-remitting Multiple Sclerosis over fifteen years. Methods: A prospective open-label cohort of 262 clinical definite patients, 78 men and 184 women, with two attacks in the past two years and a disability level DSS≤5.5 were enrolled consecutively from December 1997 to November 1999. A descriptive analysis of the cohort and individual drugs outcomes were performed. The results were compared to natural history studies of Multiple Sclerosis as controls. Results: At 15 years, one-seventh, 38/262 (14.5%) remain on the initial prescription, Betaseron, 15/131 (11.5%), Copaxone, 16/102 (15.5%) and Rebif 7/28 (25%), Avonex 0/1. 223(63.6%) had discontinued at a mean duration of 5.5(SD=4.7) years. 95/262 (36.4%) remain on a drug after switches. The DSS levels of the individual DMDs were analyzed. Conclusion: One-seventh of participants remained on their first prescription. Because of low adherence, the impact of DMDs on disease progression in the longer term cannot be verified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".