Diagnostic accuracy of Alzheimer's Questionnaire in identifying dementia among Filipinos in a tertiary hospital
Bibliographic record
Abstract
BackgroundDementia, most often due to Alzheimer's disease, is a growing concern in the Philippines. The Alzheimer's Questionnaire (AQ), an informant-based screening tool, may be particularly useful in this setting, where strong familial and caregiving ties exist. Establishing its diagnostic accuracy in Filipinos is crucial for early detection and improved care.ObjectiveTo determine the diagnostic accuracy of the AQ among Filipinos by comparing it with physician diagnoses and established cognitive assessment tools.MethodsThis retrospective cohort study included 190 Filipino patients who underwent cognitive assessments, including the AQ, Mini-Mental State Examination (MMSE-F), and Montreal Cognitive Assessment (MoCA-P), between 2022 and 2024. Diagnostic accuracy was measured using sensitivity, specificity, predictive values, and area under the curve (AUC). Cohen's Kappa assessed agreement between AQ classifications and physician diagnoses.ResultsClinico-demographic analysis suggested that age and work status may influence dementia risk, while gender and common comorbidities showed no significant associations. The AQ demonstrated high specificity (92.47%) and strong diagnostic accuracy (AUC = 0.923) in distinguishing dementia from non-dementia, performing comparably to MoCA and MMSE. However, it was less effective in detecting mild cognitive impairment (MCI).ConclusionsThe AQ is a reliable and accurate tool for dementia screening in Filipinos, though limited for MCI detection. Incorporating AQ into routine cognitive screening may enhance early dementia identification. Further studies should refine cultural adaptations and validate its role in the Philippine healthcare context.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".