Performance of Cognitive Screening Tests for Alzheimer's Disease Pathology Defined by Plasma <i>p</i> ‐tau217: A Prospective Cohort Study in Early Dementia Patients at King Chulalongkorn Memorial Hospital, Thailand
Bibliographic record
Abstract
BACKGROUND: Plasma p-tau217 is emerging as a biomarker for Alzheimer's disease (AD) diagnosis, offering a more accessible alternative to CSF and amyloid-PET. The Montreal Cognitive Assessment - Thai Version (MoCA-Thai) and Mini-Mental State Examination - Thai Version (MMSE-Thai) are widely used to detect cognitive impairment in clinical settings, but their optimal cut-off scores for identifying AD pathology, particularly with plasma p-tau217, remain unclear, especially in lower- and middle-income countries (LMICs). This study evaluates the performance of MoCA-Thai, MoCA-Memory Index Score (MoCA-MIS), and MMSE-Thai for AD diagnosis using plasma p-tau217. METHODS: We recruited patients with early-stage dementia (CDR ≤ 1) from the INDE cohort at King Chulalongkorn Memorial Hospital, Thailand (NCT06375213). AD pathology was determined using an internally validated plasma p-tau217 cutoff (>7.46 pg/mL). Cognitive assessments and Clinical Dementia Rating (CDR) scoring were conducted by trained clinical psychologists. Receiver operating characteristic (ROC) analysis and Youden's index were used to determine optimal cut-off scores. RESULTS: There were no significant differences in age, sex, or education level between AD and non-AD groups (Table 1). However, AD patients had significantly lower scores on MoCA-Thai, MoCA-MIS, and MMSE-Thai (p < 0.001). ROC analysis showed that MoCA-MIS (AUROC = 0.762) had the highest discriminative ability, followed by MoCA-Thai (AUROC = 0.738) and MMSE-Thai (AUROC = 0.725) (Figure 1). Optimal cut-off scores were determined as ≤21 for MoCA-Thai (Sensitivity = 75%, Specificity = 69%) and ≤6 for MoCA-MIS (Sensitivity = 67%, Specificity = 77%) (Table 2). CONCLUSION: In our cohort, a MoCA-Thai cut-off of ≤21 and a MoCA-MIS cut-off of ≤6 provided the best optimal sensitivity and specificity for detecting AD pathology. These findings support the integration of cognitive screening tests with plasma biomarkers to enhance early AD detection in clinical settings in Thailand, where access to advanced diagnostics is limited.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".