The Asian Cohort for Alzheimer's Disease (ACAD): Clinical Core Protocol for Assessment and Consensus Diagnosis
Bibliographic record
Abstract
BACKGROUND: The Asian Cohort for Alzheimer's Disease (ACAD) study addresses the underrepresentation of Asian Americans and Asian Canadians (ASACs) in Alzheimer's Disease (AD) research, through its investigation of genetic and non-genetic AD risk factors in diverse Asian populations, beginning with Chinese, Vietnamese, and Korean communities. Given the dearth of culturally and linguistically appropriate instruments, the ACAD Clinical Core developed a protocol that includes a Data Collection Packet (DCP). The initial protocol focused on virtual procedures to address limitations imposed by the COVID 19 pandemic. Since AD risk factors may differ among ethnic subgroups, the DCP was designed to encourage research participation by Asian older adults and to gain important information about these risk factors in Chinese, Vietnamese, and Korean populations in the US and Canada. METHOD: DCP development was led by the ACAD Clinical Core, composed of clinicians and researchers in medicine, nursing, genetics, neurosciences, and neuropsychology. At least one clinician from each ACAD recruitment site participated in protocol development. The Clinical Core worked closely with the Data Management, Training and Quality Assurance and Biosample Core to ensure successful DCP administration, sample collection, and electronic database entry. RESULT: The DCP is available in Cantonese, Korean, Mandarin, and Vietnamese and includes questions about demographics, lifestyle factors (e.g., diet, sleep, physical activities), cognitive and functional abilities, psychiatric symptoms, and medical history. This information, combined with detailed data from a neurological examination, enables study teams to reach a consensus diagnosis of Normal Control (with or without subjective cognitive concerns), Mild Cognitive Impairment, or Dementia, based on criteria described by the National Alzheimer's Coordinating Center, with a dementia diagnosis differentiated as to etiology. The DCP is designed to be culturally and linguistically appropriate, generation-specific, flexible in scheduling, and sensitive to participant anxiety and fatigue to facilitate participation. As of January 2025, ACAD has consented 1,173 participants, collected 885 samples (424 saliva and 461 blood), and completed 864 diagnoses (109 dementia, 161 MCI, 594 control). CONCLUSION: To bridge the gap of ASACs in AD research, ACAD developed the DCP and sample collection protocols. The pilot phase showcased successful virtual assessments, with expanded recruitment planned for Spring 2025.
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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 | 0.066 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".