Bibliographic record
Abstract
Abstract Background As the US population of Asian Americans and Pacific Islanders (AAPI) over the age of 65 increases, the incidence of Alzheimer’s disease and related disorders (ADRD) in this group is expected to triple between 2008 and 2030. This group includes over 4 million Filipino Americans, who comprise the largest AAPI population in California and 10 other states. Although there is scarce literature detailing the prevalence of dementia among AAPI subgroups, one study found that Filipino Americans had the highest incidence rate of dementia (Mayeda et al., 2017). Despite the expected increase in the number of Filipino Americans with ADRD, no studies to‐date have validated neuropsychological measures in the United States for speakers of Tagalog (Filipino), one of the major languages spoken by Filipino Americans and the 4th most widely spoken language in the US. A major barrier to dementia care and diagnosis is the lack of linguistically and socioculturally appropriate cognitive tasks for Tagalog speakers. To address this need, we developed and piloted the Cognitive Assessment for Tagalog Speakers (CATS), the first neuropsychological battery for the detection of ADRD in Filipino American Tagalog speakers. Method We translated, adapted, and constructed de novo to measure performance across four main cognitive domains: visual/verbal memory, visuospatial functioning, speech and language, and frontal/executive functioning. Tasks were developed with a team of bilingual English/Tagalog and bicultural Filipino American/Canadian experts, including a neurologist, speech‐language pathologist, linguist, and neuropsychologist. Result To‐date, the CATS battery has been administered to 15 healthy control participants (age 61.8 ± 5.4 yrs, 5M/10F) at the University of California, San Francisco. These preliminary results show the feasibility of the measures but also demonstrated the need to consider effects of bilingualism, language typology, and cultural factors in the interpretation of results. Conclusion This project will lay the groundwork for norming and validation studies and will help solidify connections to a population that has been underrepresented in ADRD research. As we move towards treatment and cure of ADRD, linguistically and culturally appropriate cognitive tests become even more important for equitable care.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".