Participant Survey Results in a Diverse Community‐Based Alzheimer’s Disease Blood Test Study
Bibliographic record
Abstract
BACKGROUND: A key component of Alzheimer's disease (AD) blood tests' efficacy in real-world clinical populations is ensuring its practicality and generalizability in diverse demographic groups. The Study to Evaluate Amyloid in Blood and Imaging Related to Dementia (SEABIRD) enrolled 1,122 participants to determine acceptability and validity (relative to amyloid PET) of blood plasma tests in a community-based sample of older adults, and considered the impact of factors such as age, race, education, cognition, APOE genotype, and medical conditions on outcomes of the blood test. METHOD: dementia screening interview and Montreal Cognitive Assessment [MoCA]), and a survey about their experience and perceptions. RESULT: 23.5% of 1,122 participants identified as Black or African American (AA) (Table 1) and 85.2% reported having one or more of the following medical conditions: hypertension, high cholesterol, depression, diabetes, cancer, heart attack, kidney disease, stroke. Overall, and across demographic groups, participants reported positive study experiences. Approximately 92% disagreed that the blood collection caused distress. Nearly 84% agreed they would prefer blood draws over procedures such as lumbar punctures or PET scans (Figure 1). Participants who were Black or AA, had less than a bachelor's degree, and were cognitively impaired were significantly less likely to be satisfied with compensation and ease of participation, and to express willingness to participate in future AD studies compared to white and highly educated individuals (Table 2). CONCLUSION: An AD blood test can be a feasible and widely used screening tool in a diverse population. To continue improving AD research, it is imperative to consider historically underrepresented populations and the barriers that may affect their initial and continuing participation. Broadening recruitment efforts to include varied sources across many populations, proactively addressing transportation needs, tailoring experiences to be inclusive of education levels and physical abilities, and careful consideration of compensation are some aspects of clinical studies that can create a positive impact on inclusivity and provide real-world application to AD biomarker testing.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".