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 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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".