The Global Adherence Project (GAP): a multicenter observational study on adherence to disease‐modifying therapies in patients with relapsing‐remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: most disease-modifying therapies (DMTs) for multiple sclerosis (MS) are self-injectable medications that must be taken on an ongoing basis to reduce disease activity. Thus, adherence to therapy becomes an important challenge that must be addressed to maximize benefits of therapy. This study evaluated rates of adherence to prescribed treatment and explored factors affecting adherence amongst patients with relapsing-remitting MS. METHODS: this was an observational, multicenter, multinational, phase 4 study. Patients and physicians received paper questionnaires regarding adherence to DMTs approved at the time of the study, including intramuscular interferon beta-1a (IFNβ-1a), subcutaneous IFNβ-1a, IFNβ-1b, and glatiramer acetate. Quality of life and cognition data also were collected. Multivariate analysis was conducted to identify factors associated with adherence to long-term DMTs. RESULTS: two thousand six hundred and forty-eight patients were studied, revealing an average treatment duration of 31 months. Seventy-five percent of patients (n = 1923) were adherent to therapy. The most common reasons for non-adherence were forgetting to administer the injection (50.2%) and other injection-related reasons (32.0%). Adherent patients reported better quality of life (P < 0.05) and fewer neuropsychological issues (P < 0.001) than non-adherent patients. Adherent patients had significantly shorter duration of disease (P < 0.001) and shorter duration of therapy (P = 0.005) than non-adherent patients. Women were more likely than men to adhere to treatment. CONCLUSION: identifying factors that affect adherence to prescribed treatments is the first step in improving adherence of patients with MS to therapy, thereby helping maximize the benefits of long-term DMTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".