The Asian Cohort for Alzheimer’s Disease (ACAD) Pilot Study
Bibliographic record
Abstract
Abstract Background Asian Americans and Canadians (ASACs) are the fastest growing minority group in the US and Canada. ASACs are under‐sampled in Alzheimer’s disease (AD) research. Culturally appropriate, community‐based approaches to recruit these understudied communities are urgently needed, and in 2021 the Asian Cohort for Alzheimer’s Disease (ACAD) began recruitment to the first large dementia genetics cohort to examine genetic and non‐genetic risk factors for AD among ASACs. Our clinical and community‐based participatory research (CPBR) scientists have a long collaborative history and specific experience in leading AD and CBPR research. Method The ACAD pilot study has 8 recruiting sites (6 US and 2 Canadian), a coordinating site, and an analysis site. ACAD piloted a data collection packet (DCP) and pre‐screening/sample collection procedures. The Outreach workgroup translated the forms and an outreach campaign into Chinese (Mandarin and Cantonese), Vietnamese and Korean. Data Management created a central RedCap database. The Training workgroup developed a curriculum for the administration of the DCP and for culturally appropriate approaches to recruitment. We recruited in collaboration with community partners, clinics, and nursing homes that serve Asian communities. Result ACAD’s pilot study has consented 216 participants (142 Chinese, 20 Vietnamese and 54 Korean), and 126 (58%) have completed the DCP. The majority the consent (64.3%) were given by women. The age range of the sample is 60‐93 years. 60.3% have college or graduate level education. 101 of the 126 participants provided saliva (51) or blood (50) biosamples. Data entry is complete, with Consensus Diagnoses fully reviewed on 18 participants. Among 34 diagnosed participants, there are 19 healthy controls, 11 Subjective Cognitive Complaints, 3 Mild Cognitive Impairments, and 1 Probable or Possible AD case. Conclusion Lessons learned during the pilot phase of ACAD will provide guidance for future studies to explore risk factors for AD and related dementias. In collaboration with ongoing consortium efforts in Alzheimer’s Disease Genetics Consortium (ADGC), insights from ACAD may identify potential novel, population‐specific therapeutic pathways for AD. Our long‐term goal will be to expand ACAD to have a larger sample size and include other Asian American subgroups .
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".