Sex, racial, and ethnic disparities in motor neuron disease: clinical trial enrolment
Bibliographic record
Abstract
Objective Motor neuron disease (MND) is a group of neurological diseases, the majority being amyotrophic lateral sclerosis (ALS), with varying clinical presentations across demographics. Clinical trial enrollment reflecting global disease burden improves understanding of diverse presentations and aids personalized therapy development. We assessed the sex, racial, and ethnic composition of MND/ALS clinical trial participants relative to global disease burdens.Methods We searched ‘motor neuron disease OR amyotrophic lateral sclerosis’ on ClinicalTrials.gov from 02/2000–04/2024. We extracted trial (start year, study site, sponsor location, phase, masking, intervention) and demographic data (sex, race, ethnicity) from randomized interventional studies. We obtained sex-based MND/ALS disease burden estimates from the Global Burden of Disease database. For females, we calculated pooled participation-to-prevalence ratio (PPR) with 95% confidence intervals (CIs), with PPR of 0.8–1.2 indicating adequate enrollment. We used Kruskal–Wallis tests to compare demographic groups across trial characteristics.Results Of 85 trials, females comprised 37.47% (n = 5011) of 13,372 participants; the pooled female PPR was 0.97 (95% CI: 0.77–1.16). Of 41 trials (9340 participants) reporting race, 121 (1.30%) participants were Black or African American, 16 (0.17%) American Indian or Alaskan Native, and 6 (0.06%) Native Hawaiian or Other Pacific Islander. 24 trials (595 participants) reported ethnicity, with a minority of Hispanic participants (n = 153; 2.57%).Conclusions MND/ALS clinical trials had adequate female enrollment relative to global disease burdens. Race and ethnicity data were underreported. However, there were enrollment disparities of racial and ethnic groups. Increased trial leadership diversity, equitable enrollment policies, and addressing barriers to participation could improve enrollment diversity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".