Patient perspectives on switching disease-modifying therapies in the NARCOMS registry
Bibliographic record
Abstract
INTRODUCTION: The evolving landscape of disease-modifying therapies (DMTs) for multiple sclerosis raises important questions about why patients change DMTs. Physicians and patients could benefit from a better understanding of the reasons for switching therapy. PURPOSE: To investigate the reasons patients switch DMTs and identify characteristics associated with the decision to switch. METHOD: The North American Research Committee on Multiple Sclerosis (NARCOMS) Registry conducted a supplemental survey among registry participants responding to the 2011 update survey. The supplemental survey investigated reasons for switching DMT, origin of the discussion of DMT change, and which factors influenced the decision. Chi-square tests, Fisher's exact tests, and logistic regression were used for the analyses. RESULTS: Of the 691 eligible candidates, 308 responded and met the inclusion criteria (relapsing disease course, switched DMT after September 2010). The responders were 83.4% female, on average 52 years old, with a median (interquartile range) Patient-Determined Disease Steps score of 4 (2-5). The most recent prior therapy included first-line injectables (74.5%), infusions (18.1%), an oral DMT (3.4%), and other DMTs (4.0%). The discussion to switch DMT was initiated almost equally by physicians and participants. The primary reason for choosing the new DMT was based most frequently on physician's recommendation (24.5%) and patient perception of efficacy (13.7%). CONCLUSION: Participants frequently initiated the discussion regarding changing DMT, although physician recommendations regarding the specific therapy were still weighed highly. Long-term follow-up of these participants will provide valuable information on their disease trajectory, satisfaction with, and effectiveness of their new medication.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".