Comparison of Therapeutic Goals and Preferences in Multiple Sclerosis Patients and Providers Using Nominal Group Technique (P3.100)
Bibliographic record
Abstract
OBJECTIVE: To compare the therapeutic goals and preferences of multiple sclerosis (MS) patients and MS providers. BACKGROUND: Incorporating patient preferences into therapeutic decisions promotes shared decision-making and informed values-based choices. METHODS: We conducted 14 structured focus groups using Nominal Group Technique in Colorado, Massachusetts, and Georgia. Participants responded to one of three questions: goals for managing MS, decisions about disease modifying therapy (DMT), and decisions about changes in managing MS. Responses were shared, consolidated, and ranked. Weights were assigned and scores summed to develop a prioritized list for each meeting. RESULTS: 48 ethnically diverse MS patients (79[percnt] female, 54[percnt] white) and 40 providers with subspecialty training in MS participated, generating an average of 33 responses per meeting. The most important goals for patients were achieving independence and avoiding fatigue, vision loss, disabling relapses, and bladder/bowel issues. Most of these specific goals were not included in providers lists, who more broadly prioritized preventing disability and inflammation. For DMT decisions, both patients and providers prioritized effectiveness in slowing the disease process, but patients also prioritized impact on quality-of-life and avoiding long-term risks and side-effects, while providers prioritized a confirmed MS diagnosis and disease aggressiveness. For decisions about changing treatment, both patients and providers prioritized slowing the progression of disability. Uniquely, patients prioritized mental function, avoiding new symptoms, ability to manage symptoms, and new or better treatments, while providers prioritized decrease in new MRI activity and relapse rate. CONCLUSIONS: There were differences in therapeutic goals and preferences between patients and providers as well as differences in how similar preferences were articulated. Patients tended to focus on specific symptoms and side-effects, whereas providers focused more broadly on disability and disease activity, using summary terms. This suggests a need for tools to bridge communication gaps in discussions about MS treatment. Study supported by Biogen
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".