青年卒中后认知障碍的危险因素分析 Analysis of Risk Factors of Post-stroke Cognitive Impairment in Young Stroke Patients
Bibliographic record
Abstract
目的 探讨青年卒中后认知障碍（post-stroke cognitive impairment，PSCI）的危险因素。 方法 连续收集2016年12月-2017年12月在天津市环湖医院神经内科住院的18～45岁的缺血性卒中 患者165例，记录患者人口学资料、血管危险因素、实验室检查资料，所有患者于发病14 d内行蒙特利 尔认知评估量表（Montreal cognitive assessment scale，MoCA）测评，根据MoCA评分<26分认为认知功 能障碍，分为PSCI组和认知功能正常组，采用多因素Logistic回归分析青年PSCI的独立危险因素。 结果 研究共纳入165例青年卒中患者，其中男性128例（77.6%），平均年龄（40.02±5.20）岁。青年 卒中发病14 d内出现PSCI的比例为64.8%（107例）。多因素Logistics回归分析显示较低受教育程度[比 值比（odds ratio，OR）6.752，95%可信区间（confidence interval，CI）1.385～32.906，P =0.018]、入院 时NIHSS评分较高（OR 1.660，95%CI 1.372～2.009，P＜0.001）、既往缺血性卒中（OR 3.728，95%CI 1.376～10.104，P =0.010）是青年PSCI的独立危险因素。 结论 青年PSCI的患病率仍较高，较低受教育程度、入院时NIHSS评分较高、既往缺血性卒中的患者 PSCI风险更高。 Abstract: Objective To investigate the risk factors of post-stroke cognitive impairment (PSCI) in young ischemic stroke patients. Methods Young patients (age 18-45 years) with ischemic stroke in neurology department of Tianjin Huanhu hospital from December 2016 to December 2017 were enrolled consecutively. Demographic information, vascular risk factors, laboratory test results were collected. All of the patients were assessed by Montreal cognitive assessment (MoCA) scale within 14 days from onset. According to the MoCA score, all patients were divided into PSCI (MoCA score <26) group and normal cognitive function group. Multivariate Logistic regression analysis was used to analyze the independent risk factors of young PSCI. Results A total of 165 young patients (mean age 40.02 ± 5.20; 77.6% male) were identified, and 64.8% (n =107) had PSCI within 14 days from onset. Multivariate Logistic regression analysis showed that the lower level of education [odds ratio (OR) 6.752, 95% confidence interval (CI) 1.385-32.906, P =0.018], higher NIHSS score on admission (OR 1.660, 95%CI 1.372-2.009, P <0.001), previous ischemic stroke (OR 3.728, 95%CI 1.376-10.104, P =0.010) were independent risk factors of PSCI in young adult stroke patients. Conclusions The prevalence of PSCI in young people is still high, especially those with lower level of education, higher NIHSS score on admission and previous ischemic stroke, should be paid more attention to screen PSCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".