Diagnostic performance of the Brief Cognitive Screening Battery-Indonesian version in detecting cognitive impairment
Bibliographic record
Abstract
Neuropsychological and functional assessments are crucial for identifying the transition from healthy aging to dementia. While brief cognitive batteries have become popular for their practicality, most have been developed in high-income countries, neglecting the diverse educational backgrounds found in developing nations.This study focuses on the Brief Cognitive Screening Battery (BCSB) adapted for Indonesia (BCSB-INA), aiming to investigate its diagnostic accuracy in detecting cognitive impairment among older adults.This cross-sectional study was conducted at the Memory Clinic of Universitas Sumatera Utara Hospital from January to August 2024, including participants aged 50 and above. Subjects underwent cognitive assessments using MoCA-INA and BCSB-INA. Data analysis involved ROC curves to evaluate the tests' accuracy.A total of 140 subjects were included, with significant differences in cognitive test scores between those with cognitive impairment and normal individuals. The BCSB-INA demonstrated good diagnostic performance, with an AUC of 0.875 when including the Clock Drawing Test (CDT) and 0.810 without it. The development of a multivariate model further enhances its diagnostic capabilities, allowing for more tailored intervention strategies.The BCSB-INA represents an important improvement in cognitive assessment for older adults in Indonesia, showing good sensitivity and specificity. Continued research and updates to cognitive assessment tools are crucial to meet the increasing demand for effective dementia screening in diverse populations.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".