The Asian Cohort for Alzheimer’s Disease pilot study at University of California San Francisco: A focus on recruitment of Chinese American older adults
Bibliographic record
Abstract
Abstract Background Asian American and Canadian (ASAC) populations have long been under‐represented in Alzheimer’s disease (AD) research. The Asian Cohort for Alzheimer’s Disease (ACAD) is the first large US and Canada cohort to study genetic and non‐genetic AD risk factors specifically for older ASAC persons. The University of California San Francisco (UCSF) is one of ACAD’s largest recruitment sites for Chinese American participants. Method The UCSF ACAD team has adopted a collaborative multifaceted approach to outreach, recruitment, and data collection including a) recruitment from community partners, social media platforms, clinic referrals, CARE (a research recruitment registry for Asian Americans, Native Hawaiians, and Pacific Islanders), and other ongoing studies (e.g., UCSF’s Alzheimer’s Disease Research Center); b) coordination of multilingual and culturally appropriate outreach events with multiple San Francisco Bay Area community organizations that serve the Chinese American community; c) pilot of ACAD’s data collection packet and procedures in two testing modes (in‐person, on‐line) and three spoken languages (Cantonese, English, Mandarin). Result As of January 2023, UCSF has enrolled 121 participants, 116 (95.87%) of whom have completed their visits since we began enrollment in July 2021. Out of the 116 participants, 80 (68.97%) were co‐enrolled with other ongoing studies, 21 (18.10%) were recruited from outreach events in the community, and 15 (12.93%) participated in ACAD through CARE registry, clinic referrals, or social media platforms. The age of our cohort ranges from 60‐90 years (m = 70.45 years). Most of the participants were females (72.4%) and had a college or graduate level education (68.97%). Approximately 50% of the participants completed the study in Mandarin, 32.76% in English, and 17.24% in Cantonese. Nearly two‐thirds of the participants completed their visits virtually. Consensus diagnoses were fully reviewed on 106 participants, including 61 cognitively normal controls, 16 with subjective cognitive complaints, 23 with mild cognitive impairments, and 6 with AD diagnosis. Conclusion ACAD aims to enhance the representation of ASAC communities in AD research. The use of culturally and linguistically tailored approaches has and will continue to assist us in outreach, recruitment, and data collection efforts as we aim to expand our sample size and collect longitudinal data.
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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 | 0.007 | 0.005 |
| 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 | 0.001 | 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".