Enhancing diversity of clinical trial populations in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Demographic characteristics, social determinants of health (SDoH), health inequities, and health disparities substantially influence the general and disease-specific health outcomes of people with multiple sclerosis (MS). Participants in clinical trials do not represent all people with MS treated in practice. OBJECTIVE: To provide recommendations for enhancing diversity and inclusion in clinical trials in MS. METHODS: We held an international workshop under the Auspices of the International Advisory Committee on Clinical Trials in MS (the "Committee") to develop recommendations regarding diversity and inclusivity of participants of clinical trials in MS. Workshop attendees included members of the Committee as well as external participants. External participants were selected based on expertise in trials, SDoH, health equity and regulatory science, and diversity with respect to gender, race, ethnicity, and geography. RESULTS: Recommendations include use of diversity plans, community engagement and education, cultural competency training, biologically justified rather than templated eligibility criteria, adaptive designs that allow broadening of eligibility criteria over the course of a trial, and logistical and practical adjustments to reduce study participant burden. Investigators should report demographic and SDoH characteristics of participants. CONCLUSION: These recommendations provide sponsors and investigators with methods of improving diversity and inclusivity of clinical trial populations in MS.
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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 | 0.257 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".