Preferred Features of Oral Treatments and Predictors of Non-Adherence: Two Web-Based Choice Experiments in Multiple Sclerosis Patients
Bibliographic record
Abstract
BACKGROUND: Oral disease modifying therapies (DMTs) for multiple sclerosis (MS) differ in efficacy, tolerability, and safety. OBJECTIVE: We sought to understand how these attributes impact patient preference and predicted DMT non-adherence among oral-naïve MS patients. METHODS: Adult MS patients from the "PatientsLikeMe" Web-based health data-sharing platform completed a discrete choice exercise where they were asked to express their preference for one of three hypothetical oral DMTs, each with a certain combination of levels of tested attributes. Another Web-based exercise tested a number of possible drivers of non-adherence, mainly side effects. Data from an MS clinic were used to adjust for sample bias. Respondents' preferences were analyzed using Hierarchical Bayesian estimation. RESULTS: A total of 319 patients completed all questions. Most respondents were female (77.7%, 248/319) with mean age 48 years (SD 10). Liver toxicity was the attribute that emerged as the most important driver of patient preference (25.8%, relative importance out of 100%), followed by severe side effects (15.3%), delay to disability progression (10.7%), and common side effects (10.4%). The most important drivers of predicted non-adherence were frequency of daily dosing (17.4% out of 100%), hair thinning (14.8%), use during pregnancy (14.1%), severe side effects (13.8%), and diarrhea (13.0%). CONCLUSIONS: Understanding the important concerns expressed by patients may help health care providers to understand and educate their patients more completely about these concerns. This knowledge may therefore improve both choices of appropriate therapy and adherence to therapy over time.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".