Asian cohort for Alzheimer’s disease (ACAD) data collection: Rationale, design and validation
Bibliographic record
Abstract
Abstract Background As the first large Alzheimer’s Disease (AD) genetics cohort for Asians in the United States and Canada, the Asian Cohort for Alzheimer’s Disease (ACAD) has adapted existing instruments to collect data on sex, genetics, medical history, and lifestyle risk factors. We hypothesize that any and all of these factors impact AD risk differently for Asians. Initially ACAD focuses on US and Canadian participants of Chinese, Korean, and Vietnamese ancestry. Method ACAD’s Clinical Workgroup reviewed commonly used dementia research questionnaires, including the NACC Uniform Data Set, Religious Order Study, and NIA‐Late‐Onset AD to select, and in some cases adapt, questions that are most appropriate for ACAD. A combination of AD clinical researchers and community outreach specialists reviewed the data collection packet, administration procedures, and consensus diagnosis process for cultural sensitivity without sacrificing scientific rigor to gather non‐genetic and lifestyle risk factor data. Result We designed the ACAD Data Collection Packet (DCP) to create a friendly dialogue with participants through a socio‐culturally and linguistically sensitive 3‐part process. The DCP consists of some instruments already validated in Chinese, Vietnamese and Korean languages to screen for cognitive impairment (Cognitive Abilities Screening Instrument (CASI) and Modified Mini‐Mental State (3MS)). Part A collects origins, Mediterranean diet compliance, and Clinical Dementia Rating scores. The self‐administered Part B uses an adaptation of the Rush Cognitive Activity Questionnaire on early‐life enrichment. Part C structures the collection of medical history and cognitive testing with CASI vs 3MS, the Common Objects Memory Test, Category (not Letter) Fluency, and the 15‐item Clock Drawing Task. This assessment is designed to be administered either remotely or face to face. Conclusion Over the next year, we will pilot the ACAD Data Collection Packet, to set a community standard for dementia screening among Asian Americans and Canadians that can support data harmonization across epidemiological studies. While ACAD translation accommodates Chinese, Korean, and Vietnamese at this stage, we look forward to implementing additional major Asian languages.
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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 | 0.178 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".