目的:构建晚期癌症患者预立医疗照护计划(Advance Care Planning,ACP)并进行初步验证,为临床医护人员开展ACP提供参考依据。方法:2024年1月—9月,基于文献综述、善终照护框架、专家会议法形成晚期癌症患者ACP文本。于2024年12月至2025年...目的:构建晚期癌症患者预立医疗照护计划(Advance Care Planning,ACP)并进行初步验证,为临床医护人员开展ACP提供参考依据。方法:2024年1月—9月,基于文献综述、善终照护框架、专家会议法形成晚期癌症患者ACP文本。于2024年12月至2025年1月,采用目的抽样法招募广州市某三级甲等医院25名晚期癌症患者完成ACP文本并评价。结果:ACP文本包括文本介绍、知情同意、生命质量愿望、医疗照护指示、生命尊严嘱托、爱的嘱托签订、健康评估7个一级主题和33个二级主题。患者对文本的可行性、适用性、满意度、意义评分分别为(4.91±0.32)、(4.80±0.52)、(4.75±0.66)、(4.93±0.63)分,92%的患者认为该文本很有意义。结论:基于善终照护框架和我国文化构建的ACP文本具有科学性和可行性,可为临床医护人员开展ACP提供参考。展开更多
Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have becom...Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.展开更多
文摘目的:构建晚期癌症患者预立医疗照护计划(Advance Care Planning,ACP)并进行初步验证,为临床医护人员开展ACP提供参考依据。方法:2024年1月—9月,基于文献综述、善终照护框架、专家会议法形成晚期癌症患者ACP文本。于2024年12月至2025年1月,采用目的抽样法招募广州市某三级甲等医院25名晚期癌症患者完成ACP文本并评价。结果:ACP文本包括文本介绍、知情同意、生命质量愿望、医疗照护指示、生命尊严嘱托、爱的嘱托签订、健康评估7个一级主题和33个二级主题。患者对文本的可行性、适用性、满意度、意义评分分别为(4.91±0.32)、(4.80±0.52)、(4.75±0.66)、(4.93±0.63)分,92%的患者认为该文本很有意义。结论:基于善终照护框架和我国文化构建的ACP文本具有科学性和可行性,可为临床医护人员开展ACP提供参考。
基金Under the auspices of the Natural Science Foundation of China(No.32371875,32001249)。
文摘Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.
