我国老年人认知水平变化轨迹及其分化

目的 探究影响我国老年人认知水平的变化趋势，分离出年龄、队列效应。方法 基于CLHLS（2002—2018）多重队列追踪数据，以Stata16.0软件为工具，运用分层生长曲线模型进行统计分析。结果 本研究发现，个体行为生活方式、社会经济地位、性别、慢性病数量对认知水平均具有统计学意义；年龄、队列对认知水平的变化具有独立效应；随着年龄的增长，我国老年人认知水平下降，认知水平的城乡、性别差异明显；较年轻出生队列的老年人认知水平较好，认知水平的城乡差异随着队列的年轻化而变大，性别差异在较年轻队列有略微缩小的趋势。结论 影响认知水平因素复杂，认知障碍会增加医疗成本及照护负担，因此需准确把握老年认知水平的变化规律与作用路径，从而为卫生服务、养老保障、长期医疗照护的资源配置提供科学依据。     

Measuring Psychological Inflexibility of Suicidal Thoughts: The Acceptance and Action Questionnaire for Suicidal Ideation (AAQ-SI)

Cognitive Therapy and Research - Despite interest in psychological inflexibility as a marker of suicide risk, no measure of psychological inflexibility specific to SI exists. The present study...     

“中国科技期刊卓越行动计划”领军期刊与对标期刊的指标特征分析与发展建议

【目的】 通过比较2019年获得“中国科技期刊卓越行动计划”领军期刊项目资助的期刊(以下简称“领军期刊”)与其对标期刊,探索我国科技期刊缩小与世界顶级期刊差距的对策,为我国一流科技期刊建设提供参考和借鉴。【方法】 利用Web of Science (WoS) 数据库和期刊官网,检索并总结领军期刊与其对标期刊的各项指标数据,分析领军期刊最近5年来的发展情况。【结果】 22种领军期刊的主要指标数据明显上升。2020年领军期刊共发表Article 和Review文章4025篇,比2019年的2630篇增长53.0%,2020年刊均载文量为183篇,比2019年的120篇增长53.0%;领军期刊总被引频次由2019年的99767 次大幅增长至2020年的153610次,增幅达54.0%;2020年领军期刊的平均被引半衰期为3.73年,而对标期刊的平均被引半衰期为9.35年。2020年,16种领军期刊跻身JCR学科排名前10%,10种领军期刊跻身JCR学科排名前5%。【结论】 领军期刊应构建良性的期刊发展生态、动态调整期刊的发展目标、紧扣国家战略和重大需求布局期刊发展方向、重视人才发展与储备、构建自主可控的国际传播平台与渠道,以完成引领行业发展的使命任务,实现可持续发展,从而带动我国期刊行业的整体发展与进步。     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.