The neuropsychological characteristics and regional cerebral blood flow of vascular cognitive impairment‐no dementia
Bibliographic record
Abstract
OBJECTIVE: To investigate the neuropsychological characteristics of VCI-ND and to analyze the relationship between deficit pattern and regional cerebral blood flow (rCBF) in various VCI-ND subtypes defined by cognitive features. METHODS: 69 subjects diagnosed with VCI-ND were recruited, then further classified into four subtypes: amnestic VCI-ND with single memory impairment (subtype I, n = 19), amnestic VCI-ND with multi-domain impairment (subtype II, n = 27), non-amnestic VCI-ND with single domain impairment (subtype III, n = 16), and non-amnestic VCI-ND with multi-domain impairment (subtype IV, n = 7) according to their cognitive profile. Xenon-CT scan was administered to 31 VCI-ND patients (11 of subtype I, 12 of subtype II and 8 of subtype III) and 10 normal controls (NC) to evaluate rCBF. RESULTS: The rate of different cognitive domains impairment in VCI-ND group ranged from 17 to 66%, lowest in clock drawing test and highest in time of modified version of trails making test A and maze tracing compared with NC, significant reduced rCBF was found in bilateral temporal lobe and thalamus, left periventricular white matter and caudate of subtype I, and in left temporal lobe and lenticular nucleus, bilateral periventricular white matter, white matter adjacent to left posterior horn of lateral ventricular and right caudate of subtype III, while significant reduced rCBF of subtype II was found in left subfrontal white matter, bilateral subtemporoparietal white matter, right lenticular nucleus, and in both regions of subtype I and III. CONCLUSIONS: The manifestation of rCBF in VCI-ND subtypes was consistent with performance of neuropsychological assessment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".