Analysis of current status and influencing factors of elderly stroke patients' attention
Bibliographic record
Abstract
Objective To explore the attention level and influencing factors of elderly stroke patients. Methods From September 2019 to September 2020, 178 elderly stroke patients over 60 years old who were hospitalized in the Department of Neurosurgery and Neurology of a grade A hospital in Tangshan City, Hebei Province were selected as the research objects. Pittsburgh Sleep Quality Index (PSQI) and hospitals were used. Research tools such as the Anxiety and Depression Scale (HAD) and the Montreal Cognitive Assessment Scale (MoCA) were used for investigation and research. Use SPSS 25. 0 statistical software for statistical analysis. Results The results of the study showed that gender, drinking alcohol, sleep quality, anxiety, depression, stroke type and cognitive impairment were the main influencing factors for attention disorders in the elderly after stroke. Conclusion This study found that the research subjects had a higher incidence of attention disorders, and older stroke patients who were men, usually drinking alcohol, poor sleep quality, anxiety, depression, and ischemic stroke with cognitive impairment were more likely to have attention dysfunction. (目的 探讨老年脑卒中患者的注意力水平及影响因素。方法 选取2019年9月～2020年9月河北省唐山市某三甲医院神经外科、神经内科住院的178例60岁以上的老年脑卒中患者为研究对象, 应用匹兹堡睡眠质量指数量表(PSQI)、医院焦虑抑郁量表(HAD)、蒙特利尔认知评估量表(MoCA)等研究工具进行调查研究。使用SPSS 25. 0统计软件进行统计分析。结果 研究结果显示: 性别、是否饮酒、睡眠质量、有无焦虑、有无抑郁、卒中类型、是否有认知功能障碍是卒中后老年人注意力障碍的主要影响因素。结论 老年脑卒中患者注意力障碍发生率较高, 且男性、平常习惯饮酒、睡眠质量差、有焦虑、抑郁、及缺血性脑卒中并有认知功能障碍的老年脑卒中患者更易发生注意力障碍。)
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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 | 0.000 | 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) | 0.002 | 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".