脑微出血危险因素及其与认知功能的相关性研究 Risk Factors of Cerebral Microbleeds and Its Correlation with Cognitive Impairment
Bibliographic record
Abstract
目的 探讨脑微出血（cerebral microbleeds，CMBs）患者认知功能损害的临床特征及其相关因素。 方法 研究对象来自2016年6月-2017年12月就诊于首都医科大学附属北京同仁医院的30例CMBs 患者和30例年龄、性别、受教育程度相匹配的对照组，采用简易智力状态量表（mini-mental state examination，MMSE）和蒙特利尔认知量表（Montreal cognitive assessment，MoCA）评价认知功能。采 用Fazekas量表评价脑白质病变的严重程度。CMBs组采用微出血解剖评分量表（microbleed anatomical rating scale，MARS）评估CMBs严重程度。分析CMBs严重程度与脑白质病变严重程度及认知障碍的相 关性。 结果 CMBs组MMSE[（24.9±2.7）分 vs（28.0±1.8）分，P =0.002]和MOCA[（23.6±3.4）分 vs （26.2±3.0）分，P =0.003]评分均显著低于对照组；脑白质病变的严重程度（Fazekas评分）与CMBs的严 重程度呈正相关（r =0.431，P =0.03）；MOCA评分与CMBs的严重程度呈负相关（r =-0.52，P =0.02）。 结论 CMBs患者存在明显的认知功能障碍，MOCA评分与脑微出血的严重程度负相关；脑白质病变程 度与CMBs的严重程度正相关。 Abstract: Objective To examine the clinical characteristics and correlative factors of cognitive dysfunction in patients with cerebral microbleeds (CMBs). Methods A total of 30 patients with SVD and 30 age, sex and education-matched control subjects were recruited consecutively from department of neurology, Beijing Tongren hospital. The cognitive function of all participants were evaluated by neuropsychological tests, including mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA). The severity of white matter lesions was evaluated by the Fazekas scale, and the severity of CMBs by the microbleed anatomical rating scale (MARS). The correlation between the severity of CMBs and white matter lesions/ cognitive function were analyzed. Results We found that CMBs was related to global cognitive function deficits. The severity of CMBs was positively related to the severity of white matter lesions (r =0.431, P =0.03). The severity of CMBs was negatively related to the score of MoCA (r =-0.52, P =0.02). Conclusion CMBs was closely related to cognitive impairment. The severity of CMBs was negatively related to the score of MoCA, and the severity of CMBs was positively related to the severity of white matter lesions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".