Adherence to the immunomodulatory drugs for multiple sclerosis: contrasting factors affect stopping drug and missing doses
Why this work is in the frame
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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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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| 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 it