Correlation between risk factors of cognitive dysfunction and blood pressure variability after acute ischemic stroke in northwest Shanghai
Bibliographic record
Abstract
：Objective: To explore the relationship between risk factors of cognitive dysfunction and blood pressure variability after acute ischemic stroke in northwest Shanghai to establish a model for early identification of high-risk groups of cognitive dysfunction and formulation of more targeted prevention and treatment measures. Methods: Spearman test was used to evaluate the correlation between blood pressure variability and Montreal Cognitive Assessment (MoCA) score in patients with acute ischemic stroke and the partial regression coefficient model was constructed based on the above independent risk factors, and the receiver operating characteristic (ROC) curve was described to analyze the relevant independent risk factors. Results: ROC curve analysis results showed that the clinical prediction model was significantly more effective than a single factor in predicting the risk of cognitive impairment after acute ischemic stroke in northwest Shanghai（P < 0.05）. Conclusion: Cognitive dysfunction after acute ischemic stroke was closely related to high Homocysteine (Hcy) levels, high standard deviation of systolic blood pressure, previous infarction history and infarction of cognitive function area in northwest Shanghai. The prediction model based on the above factors showed satisfactory value in predicting of cognitive dysfunction risk after acute ischemic stroke and there was also the correlation between cognitive function and blood pressure variability.
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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 | 0.001 |
| 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".