Race- and Sex-Based Disparities in Alzheimer’s Disease Clinical Trial Enrollment in the United States and Canada: An Indigenous Perspective
Bibliographic record
Abstract
Randomized clinical trials (RCT) involve labor-intensive, highly regulated, and controlled processes intended to transform scientific concepts into clinical outcomes. To be effective and targeted, it is imperative they include those populations who would most benefit from those outcomes. Alzheimer's disease (AD) is most detrimental to the aging population, and its clinical manifestation is influenced by socio-economic factors such as poverty, poor education, stress, and chronic co-morbidities. Indigenous populations in the United States and Canada are among the minority populations most influenced by poor socio-economic conditions and are prone to the ravages of AD, with Indigenous women carrying the added burden of exposure to violence, caregiving stresses, and increased risk by virtue of their sex. Race- and sex-based disparities in RCT enrollment has occurred for decades, with Indigenous men and women very poorly represented. In this review, we examined literature from the last twenty years that reinforce these disparities and provide some concrete suggestions and guidelines to increase the enrollment numbers in AD RCT among this vulnerable and poorly represented population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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".