Quantitative analysis of multiple sclerosis patients’ preferences for drug treatment: a best–worst scaling study
Bibliographic record
Abstract
BACKGROUND: With recent developments in drug therapy for multiple sclerosis (MS), new treatment options have become available presenting patients with complex treatment decisions. OBJECTIVES: The objective of this study was to elicit patients' preferences for different attributes of MS drug therapy. METHODS: A representative sample of patients with MS across Canada (n=189) participated in a best-worst scaling study to quantify preferences for different attributes of MS drug therapy, including delaying progression, improving symptoms, preventing relapse, minor side effects, rare but serious adverse events (SAEs), and route of administration. Conditional logit models were fitted to estimate the relative importance of each attribute in influencing patients' preferences. RESULTS: A latent-class analysis revealed heterogeneity of preferences across respondents, with preferences differing across five classes. The most important attributes of drug therapy were the avoidance of SAEs for three classes and the improvement of symptoms for two other classes. Only a smaller group of patients demonstrated a specific preference for avoiding SAEs, and route of administration. CONCLUSION: This study shows that preferences for drug therapy among patients with MS are different, some of which can be explained by experiences with their disease and treatment. These findings can help to inform the focus of interactions that healthcare practitioners have with patients with MS, as well as further drug development.
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 | 0.008 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".