The Disparities in Myasthenia Gravis Clinical Trial Enrollment in the United States and Canada
Bibliographic record
Abstract
INTRODUCTION/AIMS: The demographic and geographic representation of participants in myasthenia gravis (MG) trials has yet to be systematically reviewed. The goal of this study was to explore potential disparities in MG interventional clinical trial enrollment. METHODS: We included completed interventional clinical trials from January 2002 to December 2021 that enrolled participants with MG within the United States and Canada. Twenty-eight trials meeting these criteria were identified at Clinicaltrials.gov, and 16 trials contributed data. Study sponsors provided data for age, sex/gender, race, ethnicity, and state/province of site enrollment. RESULTS: Pooled data showed the following participant ethno-racial composition across trials: White = 79.9%, Black = 11.9%, Asian = 3.3%, Native American = 1.2%, "Other" race = 3.7%; 10.5% of participants identified as Hispanic ethnicity. Male participation was approximately 53%. Average participant age was 55.0 ± 17.0 years. The three highest enrolling US states were Texas, California, and Florida, and the highest enrolling Canadian province was Ontario. There was no enrollment in several Upper Midwest, Northern Rocky Mountain, and Southern US states. Total enrollment among White, Black, and Native American participants was proportional to the US population, whereas Hispanic and Asian participants were under-enrolled. DISCUSSION: The geographic distribution of enrollment suggests a possible concern that many patients do not have convenient access to trial centers. Strategies are needed to facilitate greater clinical trial participation among underrepresented and underserved communities that will improve generalization of study results to the overall MG population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".