The sensitive Amnesia Light and Brief Assessment (ALBA) is a valid 3-min test of 4 tasks indicative of mild cognitive deficits
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We report the development and validation of a unique, easily administered, but cognitively demanding 3-min test that does not require aids and can detect mild cognitive deficits (MCD). METHODS: The innovative Amnesia Light and Brief Assessment (ALBA) consists of 4 tasks: encoding the 6-word sentence "Indian summer brings first morning frost," sequential demonstration of 6 gestures and their immediate recall, and final recall of the original sentence. The memory ALBA score is the sum of all correctly recalled sentence words and gestures. The ALBA was performed in 590 persons older than 50 years, including 60 individuals who completed a neuropsychological battery, equally divided into patients with MCD (Montreal Cognitive Assessment [MoCA] score of 21±3 points) and matched cognitively normal (CN) individuals (MoCA of 27±2). RESULTS: Compared to CN individuals, the patients with MCD recalled fewer correct sentence words (median, 5 vs 2) and gestures (4 vs 3), and had lower memory ALBA scores (10 vs 6) (all comparisons, P<.00001). The cut-off point for the memory ALBA score was ≤8, with 90% sensitivity, 77% specificity, and an AUC of 0.90. Memory ALBA score correlated significantly with all neuropsychological tests except the Digit Span forward. The ALBA was minimally associated with education and age in the normative sample. CONCLUSIONS: The novel and efficient ALBA test was confirmed to have high discriminant and convergent validity, even in patients with mild cognitive deficits. The ALBA is an ultra-brief and universal cognitive test suitable for assessing cognitive impairment, dementia, and other conditions. It can easily be adapted to other cultures and administered under various conditions and settings in clinical practice and research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".