Preliminary testing of a patient decision aid for patients with relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
Background Multiple first-line disease modifying therapies (DMTs) are available for relapsing-remitting multiple sclerosis (RRMS), each with different characteristics. We developed an interactive patient decision aid (PtDA) to promote informed shared decision-making (SDM). Objective To test the preliminary effectiveness of the PtDA in participants with RRMS. Methods Knowledge, and decisional conflict were measured pre- and post- implementation of the PtDA, SDM after the consultation, and 6-month treatment patterns were observed. Differences in scores were analyzed using descriptive statistics and paired t-tests. Qualitative interviews with patients and neurologists were analyzed using thematic analysis. Results 52 participants were recruited: most were female (81%), 40 years of age or younger (62%), and had experienced MS for less than 5 years (56%). After participants used the PtDA, there was a significant improvement in decisional conflict (change = 1.00; p < 0.001) and knowledge (change = 2.15, p < 0.001). Nearly all patients wanted SDM, and 25 (56%) reported this occurred in their consult. Qualitative results suggested the PtDA supported both patients and neurologists in making decisions. Conclusion This pilot study suggests that PtDA use helps RRMS patients and their clinician select a DMT. Future studies will assess the feasibility of implementation and the impact of the PtDA on timely DMT initiation and longer-term adherence.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".