Adherence to interferon β-1a therapy using an electronic self-injector in multiple sclerosis: a multicentre, single-arm, observational, phase IV study
Bibliographic record
Abstract
BACKGROUND: In a multicentre, single-arm, observational, phase IV study, we evaluated 24-week treatment adherence of relapsing multiple sclerosis (RMS) patients using an electronic auto-injection device (RebiSmart(®)) for subcutaneous injection of interferon (IFN) β-1a. METHODS: A total of 162 adult participants with RMS were enrolled into the study to use RebiSmart(®) to self-administer IFN β-1a 44 μg three times weekly for a maximum of 96 weeks. The number of administered injections was recorded in the electronic device log. Adherence to treatment was defined as the administration of ≥80% of expected injections. Cognitive impairment and injection anxiety were assessed via questionnaires. RESULTS: Overall, 91.8 and 82.9% of participants were adherent to treatment at weeks 12 and 24, respectively. By weeks 12 and 24, 8.2 and 13.9% of participants had discontinued treatment. There were no statistically significant differences in adherence rates at weeks 12 and 24 according to cognitive impairment status or injection anxiety. By week 24, 69.9% of participants were less fearful of injection than when they started the study. According to participant evaluations, the absence of a visible needle, comfort settings, and the calendar for tracking the injection schedule were all important features of the RebiSmart(®) injection system. At week 24, 99.3% of participants reported that they would like to continue using RebiSmart(®) as their injector. CONCLUSIONS: RebiSmart(®) use is associated with high treatment adherence, as objectively assessed using electronic injection logs. Future research should examine if RebiSmart(®) use improves long-term treatment outcomes in RMS. This study was registered with ClinicalTrials.gov as NCT01128075, on May 20, 2010.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".