Thammasat-NECTEC-Chula’s Thai Language and Cognition Assessment (TLCA): The Thai Alzheimer’s and Mild Cognitive Impairment Screening Test
Bibliographic record
Abstract
Thammasat-NECTEC-Chula's Thai Language and Cognition Assessment (TLCA) is a cognitive paper-based test consisting of 21 tasks that cover 3 domains: memory, language, and other cognitive abilities. The TLCA follows some aspects of the existing tests (Thai Addenbrooke's Cognitive Examination-Revised (Thai-ACE-R) and the Thai Montreal Cognitive Assessment Test (Thai-MoCA)) and many parts were reconstructed to be more adapted to the Thai culture. Data obtained from the test will be able to precisely distinguish between patients with Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), and Normal healthy Controls (NC). The TLCA was tested on 90 participants (32 on the paper-based version and 58 on the computerized version) using a scoring procedure and speech features from verbal responses with machine learning classification. The scoring results showed significant difference between non-AD (NC + MCI) vs AD participants in 3 domains and could differentiate between NC and MCI, while machine classification could classify in three settings: NC vs non-NC (MCI + AD), AD vs non-AD and NC vs MCI vs AD. These promising results suggest that TLCA could be further verified and used as an efficient assessment in MCI and AD screening for Thais.Clinical relevance- The speech feature analysis of TLCA showed promising result for screening MCI and AD for Thais.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".