Optimal cutoff, cognitive impairment diagnostic performance, reliability and concurrent validity of the Ascertaining Dementia 8 (AD8) questionnaire among Latinos
Bibliographic record
Abstract
The Ascertaining Dementia 8 (AD8) is a brief informant- or self-administered questionnaire designed to screen for cognitive impairment, offering several advantages over performance-based screening tests. We analyzed cross-sectional data from a non-probabilistic sample of English- and Spanish-speaking Latinos who were either cognitively unimpaired or had a research diagnosis of mild cognitive impairment or dementia. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis, and the Youden Index was used to determine the optimal cutoff score. Internal consistency was tested with the Kuder-Richardson Formula 20, and concurrent validity with correlations to the Clinical Dementia Rating scale, Mini-Mental State Exam, and Montreal Cognitive Assessment scores. Among 46 participants, the optimal cutoff was 3 or higher for the total sample and in both language groups. At this threshold, the AD8 showed a sensitivity of 73.7% and specificity of 85.2%, with an area under the curve of 0.843. The AD8 achieved good internal consistency of 0.872 and demonstrated correlations in the expected directions with the cognitive impairment measures. The Spanish version generally outperformed the English version. The AD8 questionnaire has adequate psychometric properties and diagnostic performance among US Latinos. To our knowledge, this is the first manuscript to validate the AD8 among US Latinos. These findings support its use in healthcare settings and its applicability for multiple research purposes.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".