Validation study of the Alzheimer’s disease assessment scale–cognitive subscale (ADAS-Cog) for the Portuguese patients with mild cognitive impairment and Alzheimer’s disease
Bibliographic record
Abstract
OBJECTIVE: The Alzheimer's disease assessment scale-Cognitive Subscale (ADAS-Cog) is a battery to assess cognitive performance in Alzheimer's disease (AD) and was developed according to the core characteristics of cognitive decline in AD: memory, language, praxis, constructive ability, and orientation. The aim of this study was to explore the diagnostic accuracy and discriminative capacity of the ADAS-Cog for Mild Cognitive Impairment (MCI) and AD, using cut-off points for the Portuguese population. METHOD: The European Portuguese version of the ADAS-Cog was administrated to 650 participants, divided into a control group (n = 210), an MCI group (n = 240), and an AD group (n = 200). The clinical groups fulfilled standard international diagnostic criteria. Controls were healthy cognitive participants actively integrated in the community. The neuropsychological assessment protocol included the ADAS-Cog, the Mini Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Adults and Older Adults Functional Assessment Inventory (IAFAI). RESULTS: The ADAS-Cog revealed good psychometric indicators, and the total scores were significantly different between the three groups (p < .001: Control < MCI < AD). The optimal cut-off points established were: MCI > 9 points (AUC = .835; sensitivity = 58% and specificity = 91%) and AD > 12 points (AUC = .996; sensitivity = 94% and specificity = 98%). CONCLUSIONS: Our findings confirmed the capacity of the ADAS-Cog total score to identify cognitive impairment in AD patients, with poor sensitivity for MCI, in a Portuguese cohort.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".