Participant diversity in clinical trials of rehabilitation interventions for people with multiple sclerosis: A scoping review
Bibliographic record
Abstract
BACKGROUND: The selection and description of participants in clinical trials enables health care providers to determine generalizability of findings to the populations they serve. Limited diversity of participants in trials restricts evidence-based decision-making. OBJECTIVES: To determine the extent to which diverse participants are being included in clinical trials of rehabilitation interventions for people with multiple sclerosis (MS). METHODS: We conducted a scoping review of MS rehabilitation trials published since January 2002 using MEDLINE, CINAHL, and Web of Science. Covidence was used to facilitate the review. Article selection required randomized control design, a rehabilitation intervention, and a functional status outcome. Data extracted included details of intervention(s), outcomes, and participant selection and description using a social determinants of health framework. RESULT: A total of 243 studies were included. Exercise interventions and impairment-focused outcomes were most common. Most studies used only a MS Clinic for recruitment. Common exclusion criteria were physical or mental comorbidities, disability, age, and cognitive impairment. Participant age and sex were reported for almost all trials; reporting of other social determinants of health was atypical. CONCLUSION: MS rehabilitation trials have used limited recruitment methods, restricted samples, and reported few participant descriptors. Changes are required to enhance participant diversity and the descriptions of participant characteristics.
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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 | 0.139 | 0.335 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".