Validation of an Application-Based Cognitive Screening Test for Older Thai Adults
Bibliographic record
Abstract
INTRODUCTION: The cognitive screening usually requires a face-to-face format, which might limit its use in many circumstances. We aimed to develop a new application-based cognitive screening test (ACST) to serve as an accessible and valid tool in the community. METHODS: The ACST was developed by using paired association and digit span tests. This test was administered to 70 cognitively normal participants, 62 participants with MCI, and 64 participants with dementia. The 2nd edition of the Mini-Mental State Examination (MMSE-2) and the Montreal Cognitive Assessment (MoCA) were collected by certified psychologists. The ACST was self-administered by the participants, with a clinician providing instructions for those with dementia or technological limitations. The diagnosis was made according to DSM-5 criteria by an experienced geriatric neurologist blinded to the application score. Content validity, test-retest reliability, interrater reliability, and correlations between application scores and MMSE-2 and MoCA scores were analyzed. RESULTS: The sensitivity and specificity for distinguishing cognitively normal participants from non-normal participants were 92.9% and 70%, respectively (cutoff point ≤7). The sensitivity and specificity for distinguishing between the cognitively normal group and the MCI group were 87.1% and 70%, respectively (cut point ≤7). The sensitivity and specificity for distinguishing cognitively normal participants from participants with dementia were 93.8% and 82.9%, respectively (cut point ≤6). A cutoff point ≤6 was considered suitable for participants aged 75 years or older or with 6 or fewer years of education. DISCUSSION: The ACST is an easy-to-use and valid tool for cognitive screening in older Thai adults in clinical practice. Patients with an application score ≤7 are considered to be at risk of cognitive impairment and to require further evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".