The Asian Cohort for Alzheimer’s Disease (ACAD) study
Bibliographic record
Abstract
Abstract Background Asian Americans and Asian Canadians (ASACs) are the fastest growing minority group in the US and Canada. Roughly 21% of ASACs will be 65 years or older by 2060, underscoring the importance to understand how dementia and Alzheimer’s disease (AD) affect these large sectors of the populace. By comparison, ASACs are under‐sampled in AD research. Culturally appropriate, community‐based approaches to recruit these understudied communities are urgently needed. Method The Asian Cohort for Alzheimer’s Disease (ACAD) will be the first large dementia genetics cohort to examine genetic/non‐genetic risk factors for AD among ASACs. Our clinical and community‐based participatory research (CPBR) scientists have a long collaborative history, experience and leadership in AD research. The National Institute on Aging (NIA) has resourced our study, which will leverage national AD research resources and facilitate collaborations with international cohorts. Result ACAD consists of 8 recruiting sites (6 US and 2 Canada), a coordinating site, an analysis site, and 4 active workgroups. ACAD has developed a data collection packet (DCP) and pre‐screening/sample collection procedures. The Data Management Workgroup has implemented them into a central REDCap database. The Outreach Workgroup has translated the forms and are conducting an outreach campaign into Chinese (Mandarin and Cantonese), Vietnamese and Korean. The Training Workgroup has developed a training curriculum for the administration of the DCP and for culturally appropriate approaches to recruitment. We will recruit cases of dementia, mild cognitive impairment, subjective cognitive complaint, and controls without cognitive impairment in collaboration with community partners, clinics, and nursing homes that serve Asian communities. We will collect saliva or blood for DNA/genetics and biomarker studies. Conclusion After launching ACAD in November 2020, we built foundation materials to initiate recruitment in Spring 2021. ACAD will provide guidance for future studies to explore ASAC risk factors for AD and related dementias. In collaboration with ongoing consortium efforts in Alzheimer’s Disease Genetics Consortium, trans‐ethnic insights from ACAD may identify potential novel, population‐specific therapeutic pathways for AD. Our long‐term goal will be to extend ACAD to South Asians, Filipinos, and Japanese.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".