Patient satisfaction with an injection device for multiple sclerosis treatment
Bibliographic record
Abstract
OBJECTIVES: To develop a measure of treatment satisfaction assessing attributes specific to injected interferon-beta-1a (IFN-beta-1a) for multiple sclerosis (MS), and to test pain and instrument sensitivity to change among patients changing injection devices. MATERIALS AND METHODS: The MS Treatment Concerns Questionnaire (MSTCQ) was developed and tested with pain assessments before and 3 months after patients changed devices from Rebiject to Rebiject II. RESULTS: The MSTCQ was organized with two domains: Injection System Satisfaction and Side Effects (three subscales: Injection Site Reactions, Global Satisfaction, and Flu-Like Symptoms). Significant improvements (P = 0.002 to P < 0.001) occurred with the new injection device in all MSTCQ subscales (except Flu-Like Symptoms), and all pain measures (P < 0.0001). Clinically meaningful improvement was demonstrated in all scales, except Flu-Like Symptoms, by effect sizes (0.23-0.59). CONCLUSIONS: These statistically significant and clinically meaningful improvements in MSTCQ and pain measures show the value of technologically advanced devices in domains of concern to patients.
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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 | 0.004 | 0.014 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".