Visual Cognitive Assessment Test correlates with brain amyloid status in Alzheimer's disease-related mild cognitive impairment
Bibliographic record
Abstract
Background Assessing amyloid-β (Aβ) deposition in individuals with mild cognitive impairment (MCI) is critical for early Alzheimer's disease (AD) intervention. The Visual Cognitive Assessment Test (VCAT), a language-neutral visual-based tool, effectively identifies MCI, but its correlation with Aβ pathology remains unverified. Objective This study aimed to evaluate the ability of VCAT to distinguish between amyloid-positive (Aβ+) and amyloid-negative (Aβ−) people with different cognitive status and compare its performance with the Montreal Cognitive Assessment (MoCA). Methods In this cross-sectional analysis conducted at the First Affiliated Hospital of Sun Yat-sen University, 139 cognitively normal (CN) individuals and 231 patients with MCI were enrolled. Participants underwent baseline data registration, VCAT, Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination, Clinical Dementia Rating, amyloid PET assessments, and MRI scans. The main outcome measures considered the ability of the VCAT to distinguish between amyloid-positive and amyloid-negative MCI patients, as indicated by the area under the curve (AUC) values . Results VCAT and MoCA showed comparable efficacy in differentiating MCI from CN. For Aβ deposition discrimination, VCAT showed numerically higher accuracy than MoCA for detecting Aβ+ MCI (AUC 0.830 versus 0.797; sensitivity 72.7% versus 66.7%). VCAT's discriminative ability relied on memory and executive function domains. Shortened VCAT versions demonstrated reduced efficacy compared to the full VCAT. Conclusions VCAT correlates well with PET-detected Aβ deposition and shows marginally superior performance to MoCA in identifying Aβ+ MCI. However, abbreviated VCAT versions are less effective for Aβ detection in MCI, highlighting the need for full-test administration in clinical practice.
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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) | 0.001 | 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 | 0.000 |
| 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".