Training and Quality Assurance Procedures in the Asian Cohort for Alzheimer's Disease (ACAD) Study
Bibliographic record
Abstract
BACKGROUND: The Asian Cohort for Alzheimer's Disease (ACAD) is a multi-site collaboration to identify genetic and lifestyle risk factors for Alzheimer's disease (AD) in individuals of Chinese, Korean, and Vietnamese ancestry living in the US and Canada. To address training and quality assurance (TQA) challenges for this multilingual, multicultural cohort, the TQA core aimed to educate and train ACAD team members using a multidirectional feedback model to develop quality control measures. METHODS: ACAD recruited Chinese, Korean, and Vietnamese individuals over the age of 60. Consensus diagnoses included normal cognition, mild cognitive impairment, or dementia. To ensure standardized administration of instruments and data quality, the TQA core developed a comprehensive training protocol, tailored specifically for data collection. An initial survey determined the specific role of each team member, including collection of biospecimen and clinical data, outreach, prescreening, data management and cognitive assessment. Study materials created in English, Mandarin, Cantonese, Vietnamese and Korean languages, included data collection packets, a neuropsychological instruction manual, and cognitive mock videos. For each role, ACAD members were required to demonstrate knowledge on training quizzes for certification. Quality assurance procedures (e.g., double scoring, timely re-certification) verify data accuracy and identify error susceptibility. An all-hands Case Diagnosis and Education Meeting (ACADEME) is co-organized with the Clinical core to review unique cases and discuss challenging experiences. RESULTS: To date, training completion rates for ACAD members are as follows: prescreening and outreach, (n = 17, 74%), data management (n = 13, 57%), and biospecimen collection (n = 14, 64%). Comparing pre- and post-training quiz scores revealed significant improvements after prescreening (p = 0.006), outreach (p <0.001), data management (p <0.001), and biospecimen (p <0.001) trainings. Challenges for training investigators and staff facing a multilingual, multicultural cohort centered on developing materials with detailed instructions and designing methods to communicate and evaluate accuracy and consistency throughout the ACAD study. CONCLUSION: ACAD stands as one of the largest dementia cohort studies among Asians in North America. Ensuring standardized administration and data quality and integrity is crucial for generating meaningful, reproducible scientific outcomes. This study highlights the value of rigorous training for large cohort studies and underscores its impact on the broader scientific community.
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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 | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".