Risk factors for non-adherence to disease-modifying therapy in pediatric multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Adherence to disease-modifying therapies (DMTs) in pediatric multiple sclerosis (MS) is not well understood. We examined the prevalence and risk factors for poor adherence in pediatric MS. METHODS: This cross-sectional study recruited youth with MS from 12 North American pediatric MS clinics. In addition to pharmacy-refill data, patients and parents completed self-report measures of adherence and quality of life. Additionally, patients completed measures of self-efficacy and well-being. Factor analysis and linear regression methods were used. RESULTS: A total of 66 youth (mean age, 15.7 years) received MS DMTs (33% oral, 66% injectable). Estimates of poor adherence (i.e. missing >20% of doses) varied by source: pharmacy 7%, parent 14%, and patient 41%. Factor analysis yielded two composites: adherence summary and parental involvement in adherence. Regressions revealed that patients with better self-reported physical functioning were more adherent. Parents were more likely to be involved in adherence when their child had worse parent-reported PedsQL School Functioning and lower MS Self-Efficacy Control. Oral DMTs were associated with lesser parental involvement in adherence. CONCLUSION: Rates of non-adherence varied by information source. Better self-reported physical functioning was the strongest predictor of adherence. Parental involvement in adherence was associated with worse PedsQL School Functioning and lower MS Self-Efficacy-measured confidence in controlling MS.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".