Treatment Adherence, Persistence, and Compliance at 96 Weeks in MS Patients Using RebiSmart® for Injection of Interferon β-1a (P3.037)
Bibliographic record
Abstract
Objective: This multicenter, single-arm, observational, phase IV study evaluated 96-week treatment adherence, compliance, and persistence in adult RMS patients using RebiSmart® to self- administer IFN (44 μg TIW). Background: Efficacy of disease-modifying drugs (DMDs) for relapsing multiple sclerosis (RMS) can be limited by inconsistent dosing. RebiSmart® is the first electronic auto-injector for subcutaneous interferon β-1a (IFN) treatment of RMS. Methods: All injections were recorded in the electronic device log. Treatment adherence was calculated as 100 × the number of administered injections, divided by the expected number of injections in 96 weeks; compliance was calculated as 100 × the number of administered injections, divided by the number of injections per actual weeks on study. Exploratory logistic regression analyses assessed potential predictors of treatment adherence, persistence, and compliance at week 96. Results: 162 subjects were followed for ≤96 weeks. Mean (SD) adherence rate at week 96 was 69.5[percnt] (32.9[percnt]). This modest level of adherence reflected a sizeable discontinuation rate by week 96 (37.1[percnt]; 95[percnt] CI, 30.0[percnt] to 44.8[percnt]). However, in patients persisting on therapy, compliance at 96 weeks was 91.4[percnt] (12.1[percnt]). In logistic regression analyses, none of the factors examined (including baseline characteristics, disease history, injection anxiety, injection-site reactions, and flu-like symptoms) significantly predicted compliance at week 96. Conversely, age and time since last relapse each significantly predicted adherence and persistence. By week 96, 98[percnt] of subjects expressed a preference for continuing to use Rebismart®; the absence of a visible needle was judged the most important feature of the RebiSmart® injection system. Conclusions: RebiSmart® use is associated with high treatment compliance, as objectively assessed using electronic injection logs. Compliance was not significantly influenced by any patient, disease, or injection-experience factor examined.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".