Associations between cognitive impairment and computed tomography perfusion in different lobes in acute stroke of the anterior circulation
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment (CI) during the acute phase of stroke should not be ignored. The present study analyzed the relationship between computed tomography perfusion (CTP) in different lobes and CI during the acute phase of stroke in patients with cerebral infarction. METHODS: The present study included 125 subjects: 96 in the acute phase of stroke and 29 elderly healthy subjects as a control group. The Montreal Cognitive Assessment (MoCA) was used to evaluate the cognitive status of the two groups. The CTP scans include four parameters: cerebral blood flow (CBF), cerebral blood volume (CBV), time to peak (TTP), and mean transit time (MTT). RESULTS: The MoCA scores for naming, language and delayed recall significantly decreased only in patients with left cerebral infarctions. The MTT of the left vessels in the occipital lobe and the CBF of the right vessels in the frontal lobe were negatively related to the MoCA scores of patients with left infarction. The CBV of the left vessels in the frontal lobe and the CBF of left vessels in the parietal lobe were positively linked to the MoCA scores of patients with left infarction. The CBF of the right vessels in the temporal lobe was positively related to the MoCA scores of patients with right infarction. Finally, the CBF of the left vessels in the temporal lobe was inversely correlated with the MoCA scores of patients with right infarctions. CONCLUSION: During the acute phase of stroke, CTP was closely associated with CI. Changed CTP could be a potential neuroimaging biomarker to predict CI during the acute phase of stroke.
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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".