Protocol for the Asian Cohort for Alzheimer's Disease (ACAD) Study
Bibliographic record
Abstract
INTRODUCTION: To address knowledge gaps in Alzheimer's disease (AD) research, the Asian Cohort for Alzheimer's Disease (ACAD) Study will recruit over 5000 participants of Asian descent aged 60 or older in the United States and Canada. The current focus is on participants with Chinese, Korean, or Vietnamese ancestry, with the goal of characterizing both genetic and non-genetic risk factors. ACAD has assembled a culturally and linguistically appropriate data collection protocol, as well as a biosample collection protocol. Recruitment strategies follow community-based participatory research (CBPR) approaches to encourage research participation and engage Asian communities in brain health education. METHODS: The ACAD Clinical Core has developed a data collection packet (DCP) that gathers information on demographics, lifestyle factors, medical history, functional impairment, and cognitive status to support a dementia-related consensus diagnosis. Questionnaires and cognitive tests in the DCP are carefully selected or adapted to strike balance between alignment with existing AD cohorts and capturing the uniqueness in Asian subpopulations that may be related to AD. ACAD conducts a 2-year follow-up visit with all participants and a second 2-year follow-up visit with those participants who received a diagnosis of mild cognitive impairment at the first follow-up visit to confirm the robustness of the DCP and capture the trajectory of cognitive decline. DISCUSSION: ACAD is well positioned to address major challenges in recruitment, language barriers, and cultural appropriateness to ensure accurate assessment with the use of the DCP (developed in ACAD targeted languages) and multilingual research coordinators. Along with DNA and plasma biomarkers derived from biosamples, ACAD integrates comprehensive survey and cognitive data to facilitate multidimensional analyses. The data collection protocol is adaptable for other Asian subpopulations beyond ACAD's current focus, with an expectation of further bridging the gap in participant diversity within AD research. Highlights: Asian Americans and Asian Canadians constitute one of the fastest growing non-White older populations.Asians have been under-represented in American and Canadian AD research.The ACAD Study addresses that under-representation in research.The study investigates Chinese, Vietnamese, and Korean genetic and non-genetic risk factors for AD.The data collection protocol is adapted for Asians but maintains synergy with existing cohorts.
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 | 0.041 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.077 | 0.023 |
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".