Association of Cerebral Hypoperfusion and Poor Collaterals with Cognitive Impairment in Patients with Severe Vertebrobasilar Artery Stenosis
Bibliographic record
Abstract
Background: Effect of stenosis of vertebrobasilar artery (VBA) on cognitive function is elusive. Objective: To investigate association of cerebral hypoperfusion and poor collaterals with vascular cognitive impairment (VCI) in severe VBA stenosis patients. Methods: We consecutively enrolled patients with severe VBA stenosis confirmed by digital subtraction angiography who underwent computed tomographic perfusion (CTP) and cognitive assessments. Patients were divided into poor or good collaterals groups according to the collateral circulation status, and were grouped into different perfusion groups according to CTP. Cognitive function was measured by Montreal Cognitive Assessment (MoCA), Clock Drawing Test, Stroop Color Word Test, Trail Making Test, Digital Span Test, Auditory Verbal Learning Test, and Boston Naming Test scales. The association of cerebral perfusion and collaterals with VCI were explored. Results: Among 88 eligible patients, VCI occurred in 51 (57.9%) patients experienced. Poor collateral was present in 73 (83.0%) patients, and hypoperfusion in 64 (72.7%). Compared with normal perfusion patients, the odds ratio with 95% confidence interval for VCI was 12.5 (3.7-42.4) for overall hypoperfusion, 31.0 (7.1-135.5) for multiple site hypoperfusion, 3.3 (1.0-10.5) for poor collaterals, and 0.1 (0-0.6) for presence of posterior communicating artery (PcoA) compensated for posterior cerebral artery (PCA) and basilar artery (BA). Additionally, decreased scores of cognitive function tests occurred in patients with decompensated perfusion or poor collaterals. Conclusions: Hypoperfusion and poor collaterals were positively associated with cognitive impairment in patients with severe VBA. However, PcoA compensated for the PCA and BA had a protective role in cognitive impairment development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".