Psychometric Properties of Alzheimer’s Disease Assessment Scale-Cognitive Subscale for Mild Cognitive Impairment and Mild Alzheimer’s Disease Patients in an Asian Context
Bibliographic record
Abstract
INTRODUCTION: The purpose of the current study is to assess the psychometric properties of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) on patients with mild cognitive impairment (MCI) and mild Alzheimer's disease (AD) in a multicultural Asian context. MATERIALS AND METHODS: Sixty-four mild AD patients (mean age ± SD; 72.24 ± 7.88 years), 80 MCI patients (66.44 ± 7.45 years) and 125 healthy controls (HCs) (61.81 ± 6.96 years) participated in the study. Participants underwent a clinical interview and serial neuropsychological testing. ADAS-Cog total and subtest scores were compared across the 3 groups. Receiver operating characteristics (ROC) analysis were performed and sensitivity, specificity, positive predictive values (PPVs) and negative predictive values (NPVs) were calculated. RESULTS: Patients with MCI attained significantly worse neuropsychological test scores than healthy controls but significantly better results than patients with mild AD on ADAS-Cog total score, subtest items, and the delayed recall item (P <0.001). The best cutoff score to differentiate between MCI and HC was ≥4 (sensitivity = 0.73, specificity = 0.69, PPV = 0.90, NPV = 0.40), while the best cutoff score to distinguish between MCI and mild AD was ≥12 (sensitivity = 0.86, specificity = 0.89, PPV = 0.99, NPV = 0.32). Evidence of internal consistency of the ADAS-Cog (Cronbach α = 0.85) as well as convergent validity with the Mini-Mental State Examination (MMSE) (ρ = -0.75) and Montreal Cognitive Assessment (MoCA) (ρ = -0.81) (both P <0.001) was also found. CONCLUSION: The ADAS-Cog which is widely used in clinical trials is applicable to the Asian cohort. It is useful in the detection of MCI and mild AD as well as in distinguishing these 2 conditions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".