The Risk Factors of Cognitive Function Impairment after Cerebral Infarction
Bibliographic record
Abstract
Objective To investigate the correlative risk factors of cognitive function impairment after cerebral infarction.Methods 143 cases with acute stroke were elected and their common and clinical information were recorded.The sites,size and infarction were confirmed by CT or MRI.cognition was evaluated by MoCA,ADL scale after 3 months of cerebral infarction.Results ① The occurrence of cognition impairment was 51.05% after 3 months of cerebral infarction.②There were significantly statistical difference between VCI group and N-VCI group in frontal lobe,temporal lobe,hemorrhage of basal ganglion area and thalamencephalon(P0.01).③There were significantly statistical difference between VCI group and N-VCI group in age,education level,hypertension,diabetes,multiple infarction,multiple lesions,big lesion,left infarction,Cerebral atrophy and Cerebral white matter lesions(P0.01).④Multi-factor logistic regression analysis showed that hypertension(4.889),multiple infarction(3.604),multiple lesions(4.693),left infarction(2.974),Cerebral atrophy(3.765)and cerebral white matter lesions(2.828) were positively correlated with post-stroke cognitive mpairment(P0.05).Conclusion Cognitive dysfunction is a common complication of cerebral infarction.infarction of frontal lobe,temporal lobe,hemorrhage of basal ganglion area and thalamencephalon are liable to cause VCI,old age,low education level,hypertension,diabetes,multiple infarction,multiple lesions,big lesion,left infarction,cerebral atrophy and cerebral white matter lesions are the risk factors of VCI.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".