Determinants of non-adherence to disease-modifying therapies in multiple sclerosis: A cross-Canada prospective study
Bibliographic record
Abstract
Background: Poor adherence to the disease-modifying therapies (DMTs) for multiple sclerosis (MS) may attenuate clinical benefit. A better understanding of characteristics associated with non-adherence could improve outcomes. Objective: To evaluate characteristics associated with non-adherence to injectable DMTs. Methods: Consecutive patients from four Canadian MS Clinics were assessed at three time points over two years. Clinical and demographic information included self-reported DMT use, missed doses in the previous 30 days, health behaviors, and comorbidities. Non-adherence was defined as <80% of expected doses taken. We employed generalized estimating equations to examine characteristics associated with non-adherence at all time points with findings reported as adjusted odds ratios (OR). Results: In all, 485 participants reported use of an injectable DMT, of whom 107 (22.1%) were non-adherent over the study period. Non-adherence was associated with a lower Expanded Disability Status Scale score (0-2.5 vs 3.0-5.5, OR: 1.80; 95% confidence interval (CI): 1.06-3.04), disease duration (>= 5 vs < 5 years, OR: 2.23; 95% CI: 1.10-4.52), alcohol dependence (OR: 2.14; 95% CI: 1.23-3.75), and self-reported cognitive difficulties, measured by the Health Utilities Index-3 (OR: 1.55; 95% CI: 1.08-2.22). Conclusions: Nearly one-quarter of participants were non-adherent during the study. Alcohol dependence, perceived cognitive difficulties, longer disease duration, and mild disability status were associated with non-adherence. These characteristics may help healthcare professionals identify patients at greatest risk of poor adherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".