老年脑白质病变认知功能下降影响因素研究 Research of Influential Factors of Cognitive Function Decline in Aged Patients with White Matter Lesion
Bibliographic record
Abstract
目的 探讨老年脑白质病变（white matter lesion，WML）患者认知功能下降的影响因素及预测因子。 方法 连续登记2014年9月-2016年9月期间，郑州大学第一附属医院老年病科、神经内科门诊及住 院的无认知功能障碍的WML患者，收集患者人口学资料、血管危险因素及磁共振成像检查结果。入 组时行蒙特利尔认知评估量表（Montreal cognitive assessment scale，MoCA）及脑白质改变分级量表 （age-related white matter changes rating scale，ARWMCRs）评定。根据1年随访时MoCA量表评分分为轻 度认知功能障碍（mild cognitive impairment，MCI）组和无认知障碍组。通过单因素和多因素Logistic回 归分析，判断老年WML患者认知功能下降的影响因素及预测因子。 结果 研究共入组118例WML患者，其中男性67例，女性51例，平均年龄（68.07±3.70）岁。1年随访时 有100例（84.75％）患者保持原有认知状态不变，18例（15.25％）进展为MCI。Logistic回归分析发现高 血压病[比值比（odds ratio，OR）1.47，95%可信区间（confidence interval，CI）1.08～1.93，P =0.013）]和 糖尿病（OR 1.38，95%CI 1.01～1.88，P =0.042）是WML患者进展为MCI的独立危险因素，ARWMCRs评分 ≥8分（OR 1.84，95%CI 1.38～2.47，P =0.004）是WML患者进展为MCI的独立预测因子。 结论 高血压病和糖尿病是WML患者进展为MCI的独立危险因素，ARWMCRs评分≥8分是WML患者进 展为MCI的独立预测因子。 Abstract: Objective To explore the influential factors and predictive factors of cognitive function decline in patients with white matter lesion (WML). Methods WML patients without cognitive dysfunction from out-patients and in-patients in departments of geriatrics and neurology in the first affiliated hospital of Zhengzhou University from September 2014 to September 2016 were registered consecutively. Their general demographic data, vascular risk factors, biochemical test results and magnetic resonance imaging were collected. Montreal cognitive assessment scale (MoCA) and age - related white matter changes rating scale (ARWMCRs) evaluation were used. According to 1 year follow-up MoCA evaluation scores, the patients were divided into group with mild cognitive impairment (MCI) and group without MCI. Single factor analysis and multi-factors Logistic regression analysis were used to find out the influential factors and predictive factors of cognitive decline in aged patients with WML. Results There were 118 cases of WML including 67 males and 51 females, with mean age of 68.07±3.70 years old. At 1 year follow-up, 100 patients’ (84.75%) cognitive state remained unchanged and the rest 18 patients (15.25%) were diagnosed with MCI. Logistic regression analysis demonstrated that hypertension [odds ratio (OR)=1.47, 95%confidence interval(CI) 1.08-1.93, P =0.013] and diabetes (OR 1.38, 95%CI 1.01-1.88, P =0.042) were independent risk factors of MCI,and ARWMCRs ≥8 scores (OR 1.84, 95%CI 1.38-2.47, P =0.004) was predictive factor of MCI. Conclusion Hypertension and diabetes are independent risk factors of MCI, and ARWMCRs ≥8 scores is the predictive factor of MCI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".