腔隙性脑梗死认知功能障碍与颈动脉粥样硬化性斑块关系的研究 Relationship between Cognitive Impairment in Lacunar Infarction and Carotid Atherosclerotic Plaque
Bibliographic record
Abstract
目的 探讨腔隙性脑梗死（lacunar infarction，LI）认知功能障碍与颈动脉粥样硬化性斑块的关系。 方法 回顾性分析2017年1月-2018年11月就诊于海淀医院神经内科的符合LI诊断标准的患者171例，按照蒙特利尔认知评估量表（Montreal cognitive assessment scale，MoCA）评分分为认知功能障碍组和非认知功能障碍组，对颈动脉动脉粥样硬化性斑块与认知功能障碍之间的关系进行Logistic多因素回归分析。 结果 调整潜在混杂因素后，颈动脉动脉粥样硬化性斑块数≥2对LI患者合并认知功能障碍有独立影响（OR 2.843，95%CI 1.301～6.216，P=0.008）。有动脉粥样硬化性斑块颈动脉数≥2也是LI患者合并认知功能障碍的独立影响因素（OR 2.899，95％CI 1.311～6.409，P=0.008）。 结论 颈动脉动脉粥样硬化性斑块数、有动脉粥样硬化性斑块的颈动脉数增加，LI患者合并认知功能障碍的风险也增加。 Objective To investigate the relationship between cognitive impairment in lacunar infarction (LI) and carotid atherosclerotic plaque. Methods A retrospective analysis of 171 patients who met the LI diagnostic criteria in the Department of Neurology, Haidian Hospital from January 2017 to November 2018, and had baseline MoCA scores, were divided into cognitive impairment group and non-cognitive impairment group. Carotid ultrasonography were performed in all patients to observe carotid plaques. Multivariate Logistic regression was used to analyze the relationship between carotid atherosclerotic plaque and cognitive impairment. Results After adjusting for potential confounders, the incidence of cognitive impairment was higher in patients with ≥2 carotid atherosclerotic plaques compared with patients without carotid atherosclerotic plaque (OR 2.843, 95%CI 1.301-6.216, P=0.008). Patients with ≥2 carotid arteries with atherosclerotic plaque had a higher incidence of cognitive impairment (OR 2.899, 95%CI 1.311-6.409, P=0.008). Conclusions The number of carotid atherosclerotic plaque is an independent risk factor of cognitive impairment in LI patients. With the number of atherosclerotic plaques increasing, the risk of cognitive impairment may increase. The number of carotid artery with atherosclerotic plaque is also an independent risk factor of cognitive impairment in LI patients. With the number of carotid artery with atherosclerotic plaque increasing, the risk of cognitive impairment may increase.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".