Medication adherence in multiple sclerosis as a potential model for other chronic diseases: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine whether better medication adherence in multiple sclerosis (MS) might be due to specialised disease-modifying drug (DMD) support programmes by: (1) establishing higher adherence in MS than in other chronic diseases and (2) determining if higher adherence is associated with patient-specific or treatment-specific factors. DESIGN: Retrospective cohort study with data from 1 January 1996 to 31 December 2015. SETTING: Population-based health administrative data from three Canadian provinces. PARTICIPANTS: Individual cohorts were created using validated case definitions for MS, epilepsy, Parkinson's disease (PD) and rheumatoid arthritis (RA). Subjects were included if they received ≥1 dispensation for a disease-related drug between 1 January 1997 and 31 December 2014. MAIN OUTCOME MEASURES: Proportion of subjects with optimal adherence (≥80%) measured by the medication possession ratio 1 year after the index date (first dispensation of disease-related drug). RESULTS: 126 478 subjects were included in the primary analysis (MS, n=6271; epilepsy, n=55 739; PD, n=21 304; RA, n=43 164). Subjects with epilepsy (adjusted OR, aOR 0.29; 95% CI 0.19 to 0.45), PD (aOR 0.42; 95% CI 0.29 to 0.63) or RA (aOR 0.26; 95% CI 0.19 to 0.35) were less likely to have optimal 1-year adherence compared with subjects with MS. Within the MS cohort, adherence was higher for DMD than for chronic-use non-MS medications, and no consistent patient-related predictors of adherence were observed across all four non-MS medication classes, including having optimal adherence to DMD. CONCLUSIONS: Subjects with MS were significantly more likely to have optimal 1-year adherence than subjects with epilepsy, RA and PD, and optimal adherence appears related to treatment-specific factors rather than patient-related factors. This supports the hypothesis that higher adherence to the MS DMDs could be due to the specialised support programmes; these programmes may serve as a model for use in other chronic conditions.
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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 | 0.004 |
| 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 | 0.001 |
| 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".