Cogstate Brief Battery performance in assessing cognitive impairment in Taiwan: A prospective, multi-center study
Bibliographic record
Abstract
BACKGROUND: Early detection and accessible monitoring of dementia are crucial for timely intervention. However, traditional neuropsychological assessments are resource-intensive, and with the growing aging population, scheduling delays increased. The Cogstate Brief Battery (CBB) offers a promising, rapid screening tool, though its validation in Mandarin-speaking populations has been limited. METHODS: This prospective, multi-center study assessed the validity of the CBB in distinguishing between mild cognitive impairment (MCI) and dementia compared to healthy controls (HC) in Taiwan. Participants from three tertiary medical centers underwent comprehensive cognitive evaluation using the CBB, including the Learning/Working Memory Brain Performance Index (Lrn/WM BPI), Attention/Psychomotor Brain Performance Index (Attn/Psychomotor BPI), combined BPI, and Brain Age. RESULTS: Of 192 participants (mean age 68.7 ± 8.2 years, mean education level 11.0 ± 3.9 years, 59.9% female), the CBB showed strong discriminatory power across groups. The combined BPI demonstrated the highest AUC (0.95) for distinguishing dementia from HC, followed by 0.92 for Lrn/WM BPI, 0.91 for Brain Age difference, and 0.88 for Attn/Psychomotor BPI. A combined BPI cut-off score of 41.25 effectively differentiated between HC and cognitive impairment groups. CONCLUSION: This first validation of the CBB in Mandarin-speaking populations highlights its utility as a reliable screening tool for early cognitive impairment detection. Its use could enhance timely diagnoses, helping to streamline clinical pathways and improve patient outcomes, addressing a critical gap in dementia care in Taiwan's healthcare system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".