The development of a story recall test for distinguishing between Alzheimer’s disease and mild cognitive impairment in Thai cohort
Bibliographic record
Abstract
The story recall task requires complex cognitive functions since it imitates everyday communication. It provides promising discrimination between individuals with cognition intact, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). This study aimed to develop and examine the validity of the Thai Story Recall Test (TSR) in community-dwelling Thai older adults. A total of 98 participants were recruited and underwent the TSR with the stimulus story in the Thai context along with the neuropsychological tests including the Montreal Cognitive Assessment (MoCA), verbal fluency, and digit span tasks. Partial correlation analyses, controlling for education, demonstrated that the MoCA marginally and significantly correlates with the immediate recall scores, whereas delayed recall scores showed a statistically significant moderate correlation with the MoCA. Specifically, only delayed recall scores were statistically significant in differentiating between stages of AD pathology. Further analysis revealed that delayed recall, backward digit span, and letter fluency tasks could significantly contribute to a discriminant function. It successfully classified participants with cognitive impairment (MCI and AD together) with an accuracy of 0.87, a sensitivity of 83.3%, and a specificity of 77.1%. Thus, delayed recall in the TSR has the potential to detect cognitive deficits in Thai older adults, especially when combined with other neuropsychological measures. Moreover, screening tools for AD should encompass not only memory assessment but also language and attention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".