Adherence to the immunomodulatory drugs for multiple sclerosis: contrasting factors affect stopping drug and missing doses
Bibliographic record
Abstract
BACKGROUND: Long-term immunomodulatory drug (IMD) treatment is now common in multiple sclerosis (MS). However, predictors of adherence are not well understood; past studies lacked lifestyle factors such as alcohol use and predictors of missed doses have not been evaluated. We examined both levels of non-adherence-stopping IMD and missing doses. METHODS: This longitudinal prospective study followed a population-based cohort (n = 199) of definite MS patients in Southern Tasmania (January 2002 to April 2005, source population 226 559) every 6 months. Baseline factors (demographic, clinical, psychological and cognitive) affecting adherence were examined by logistic regression and a longitudinal analysis (generalized estimating equation (GEE)). RESULTS: Of the 97 patients taking an IMD (mean follow-up = 2.4 years), 73% (71/97) missed doses, with 1 in 10 missing > 10 doses in any 6-month period. Missed doses were positively associated with alcohol amount consumed per session (p = 0.008). A history of missed doses predicted future missed doses (p < 0.0005). Over one-quarter (27/97) stopped their current IMD, which was associated with lower education levels (p = 0.032) and previous relapses (p = 0.05). No cognitive or psychological test predicted adherence. CONCLUSIONS: There were few strong predictors of missed doses, although people with MS consuming more alcoholic drinks per session are at a higher risk of missing doses. Divergent factors influenced the two levels of non-adherence indicating the need for a multifaceted approach to improving IMD adherence. In addition, missed doses should be assessed and incorporated into clinical trial design and clinical practice as poor adherers could impact on clinical outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".