P1‐527: THE UCSF BRAIN HEALTH ASSESSMENT: A CULTURALLY APPROPRIATE AND SENSITIVE SCREENING TOOL TO DETECT COGNITIVE IMPAIRMENT IN SPANISH SPEAKERS
Bibliographic record
Abstract
Widely used cognitive screening tools often do a poor job of identifying mild cognitive impairment in Spanish-speaking individuals, especially those with low to moderate educational attainment. The BHA was designed to minimize language / ethnicity and education-related biases in the detection of cognitive impairment. This 10-minute, tablet-based screen includes subtests of memory, executive function / speed, visuospatial, and language skills, and an informant survey. It has previously been shown to correctly identify MCI in English speakers. The BHA and the Spanish Montreal Cognitive Assessment (MoCA) were administered to a Spanish-speaking group of 16 neurologically healthy older controls and 13 individuals with MCI or dementia of similar age and gender, with low to moderate levels of education (11.3 +/-4.6 years). Discriminant function analyses and receiver operating characteristic curves were calculated to compare how accurately these assessments classified the subjects as cognitively healthy versus impaired. The MoCA correctly classified 75% of subjects. The BHA correctly classified 90% of subjects. The area under the curve for the MoCA was .77 and for the BHA was .96. At 80% specificity, the sensitivities were 73% for the MoCA and 100% for the BHA. Preliminary analyses indicate that the Spanish version of the BHA accurately discriminates cognitively impaired subjects. A larger population is needed to strengthen our analyses, and to conduct further investigations into concurrent and anatomical validity of BHA subtests.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".