Health Care Science最新文献

{"title":"A Prediction Model for One-Year Disability Risk Among Community-Dwelling Older Adults in China: Integrating Physical, Metabolic, and Psychosocial Factors","authors":"Yanjun Ma,&nbsp;Chaofan Geng,&nbsp;Zhibin Wang,&nbsp;Yiwei Zhao,&nbsp;Lixin Ma,&nbsp;Pengpeng Ye,&nbsp;Leilei Duan,&nbsp;Guoping Peng,&nbsp;Yi Tang","doi":"10.1002/hcs2.70080","DOIUrl":"10.1002/hcs2.70080","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Disability in older adults, characterized by progressive limitations in performing activities of daily living, poses a significant public health challenge in aging societies. Early identification of individuals at risk is critical for implementing targeted interventions to mitigate functional decline. However, existing prediction models often prioritize disease-related physiological indicators and overlook psychosocial dimensions, limiting their practicality and scalability in community-based settings. This study aimed to develop and validate a practicable prediction model for 1-year disability risk by integrating multidimensional indicators.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A prospective community-based cohort in Beijing, functionally independent at baseline, was followed for 1 year. Disability was defined as a decline in the Barthel Index score. Potential predictors included demographic, lifestyle, clinical, physical, and psychosocial measures. The least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by logistic regression to construct the final model. Model performance was evaluated through fivefold cross-validation repeated 100 times, with assessment of discrimination and calibration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among 1003 community-dwelling older adults (mean age 68.4 ± 5.2 years; 58.3% female), 163 (16.3%) developed disability at follow-up. The LASSO regression identified seven predictors: age, dyslipidemia, gait speed, waist circumference, difficulty in lifting weights, appetite, and emotion regulation ability. The model demonstrated moderate discrimination, with an area under the receiver operating characteristic curve of 0.716 (95% CI: 0.712–0.720). Calibration curves indicated good overall agreement between predicted and observed risks. A nomogram was developed to facilitate individualized risk prediction in clinical practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study presents a practical disability risk prediction model incorporating physical, metabolic, and psychosocial factors. The model exhibits acceptable discrimination and calibration, supporting its potential for early screening and stratified management of community-dwelling older adults. Future multicenter validations are warranted to enhance generalizability and explore dynamic interventions targeting modifiable factors like emotion regulation and gait speed.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"320-329"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Applications and Future Prospects of Organ Medicine","authors":"Jiayi Zhang,&nbsp;Youlu Liu,&nbsp;Yuexin Li,&nbsp;Rican Hu,&nbsp;Runbing Mo,&nbsp;Yuyi Zhang,&nbsp;Guangliang Huang,&nbsp;Xiaoshun He","doi":"10.1002/hcs2.70090","DOIUrl":"10.1002/hcs2.70090","url":null,"abstract":"&lt;p&gt;Medical research spans multiple levels—from population and individual studies to cellular and molecular investigations—encompassing disciplines such as epidemiology, clinical medicine, cell biology, and molecular medicine. However, the organ level remains underexplored due to the historical lack of technology capable of maintaining isolated organs under physiological conditions ex situ [&lt;span&gt;1&lt;/span&gt;]. This gap has hindered a direct and precise understanding of organ function and disease mechanisms, often leading to systemic therapies with unavoidable side effects.&lt;/p&gt;&lt;p&gt;Throughout scientific history, transformative devices have catalyzed paradigm shifts. The steam engine and telescope, for example, revolutionized industry and astronomy, respectively. Similarly, ex situ machine perfusion (MP), initially developed for organ transplantation, now offers a novel platform for advancing medical science. Normothermic machine perfusion (NMP) in particular simulates in situ circulation, maintaining organ viability and functionality outside the body. This enables controlled investigation of organ physiology, disease pathogenesis, and the development of targeted therapies.&lt;/p&gt;&lt;p&gt;Dr. Xiaoshun He's group has recently proposed a new concept of Organ Medicine (Figure 1), namely, investigation and treatment of diseases at an organ level with the help of isolated or ex situ machine perfusion technology [&lt;span&gt;2&lt;/span&gt;]. Herein, we write this editorial with the aim of engaging the wider scientific community in this cutting-edge area. We will explore the potential use of this concept in both biology and medicine. We will discuss the current status and future directions of Organ Medicine in the aspects of organ research, organ therapy, as well as organ education.&lt;/p&gt;&lt;p&gt;Ex situ MP serves not only as a tool for organ repair but also as a versatile research platform. It allows detailed study of organ physiology and pathophysiology, inter-organ interactions, and provides a stable model for drug discovery and testing.&lt;/p&gt;&lt;p&gt;Organ Medicine represents a paradigm shift beyond transplantology, integrating ex situ perfusion technology into research, therapy, and education. By enabling organ-level investigation, targeted treatment, and immersive training, this field holds promise to deepen our understanding of human physiology and disease, accelerate therapeutic innovation, and redefine medical practice. Its interdisciplinary nature will further foster convergence with bioengineering, biology, and related sciences, ultimately contributing to improved health outcomes and quality of life.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Jiayi Zhang:&lt;/b&gt; supervision, writing – review and editing, methodology, validation, visualization, formal analysis. &lt;b&gt;Youlu Liu:&lt;/b&gt; writing – review and editing, visualization, formal analysis, project administration. &lt;b&gt;Yuexin Li:&lt;/b&gt; software, formal analysis, project administration. &lt;b&gt;Rican Hu:&lt;/b&gt; formal analysis, writing – original draft. &lt;b&gt;Runbing Mo:&lt;/b&gt; writing – origi","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"261-265"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-Term Trends and Projections of the Global Epilepsy Burden: Insights From the Global Burden of Disease Study 2021","authors":"Du Cai,&nbsp;Xianze Li,&nbsp;Qiwen Yuan,&nbsp;Zhongming Lian,&nbsp;Chenyu Zhao,&nbsp;Baotian Zhao,&nbsp;Xiu Wang,&nbsp;Chao Zhang,&nbsp;Lin Sang,&nbsp;Wenhan Hu,&nbsp;Xiaoqiu Shao,&nbsp;Jianguo Zhang,&nbsp;Shichuo Li,&nbsp;Jiajie Mo,&nbsp;Kai Zhang","doi":"10.1002/hcs2.70081","DOIUrl":"10.1002/hcs2.70081","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Epilepsy is a common neurological disorder imposing significant global health burdens. A comprehensive understanding of its temporal and spatial trends is critical for informing policies and guiding interventions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study assessed the burden of epilepsy from 1990 to 2021 worldwide, in Asia, and in China, and projected trends to 2050. We used data from the Global Burden of Disease 2021 study to evaluate the incidence, prevalence, mortality, and disability-adjusted life years (DALYs) related to epilepsy. Joinpoint regression, age-period-cohort analysis, and autoregressive integrated moving average forecasting were used to examine temporal trends and project future patterns. We also assessed the influence of socioeconomic development, health service coverage, and risk factors on the epilepsy burden, focusing on regional disparities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>From 1990 to 2021, the incidence and prevalence of epilepsy showed a significant upward trend, while mortality and DALY rates showed a significant downward trend. China exhibited sharper reductions in mortality (average annual percent change = −2.64) and DALY rates (average annual percent change = −1.79). Inequalities persist, with low socio-demographic index countries showing a disproportionately higher epilepsy burden. In China, improved universal health coverage and socioeconomic development are correlated with a decreasing burden. Alcohol use remains a key modifiable risk factor, particularly among men. Projections suggest a continued shift in the epilepsy burden toward older adults, with those aged ≥ 70 years contributing the largest share of future cases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Epilepsy remains a major global health concern with a substantial and uneven disease burden. Comprehensive burden analyses are essential for guiding public health policy, optimizing resource allocation, and developing targeted prevention and intervention strategies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"269-283"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Deployment of DeepSeek-Based Applications in Healthcare: A Chinese Perspective","authors":"Maoxin Lv,&nbsp;Ning Li,&nbsp;Hui Zhang,&nbsp;Chao Liu,&nbsp;Ge Wu,&nbsp;Zheng Zhu,&nbsp;Yao Zhu,&nbsp;Mengchun Gong","doi":"10.1002/hcs2.70068","DOIUrl":"10.1002/hcs2.70068","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Artificial intelligence (AI) is already showing enormous potential in the healthcare sector. Generative AI, particularly, is accelerating the sector's digital transformation by delivering intelligent decision support, automated diagnosis, and optimized resource allocation. DeepSeek-R1, a large-language model with a strong performance-to-cost ratio, has gained popularity as a foundation model in Chinese hospitals. However, the deployment of generative AI remains challenging, and hospitals continue to lack clear guidance on how to select among deployment architectures and how to balance computational demand with cost. Deeper, data-driven analysis is therefore warranted to inform future roll-outs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study surveyed AI deployment across Chinese hospitals, with a focus on DeepSeek's potential applications under national policies. Data were collected from the top 20 hospitals, regional centers, and township hospitals via official WeChat platforms. The survey examined deployment strategies, model versions, and platform choices, while keeping in account hospital needs, data resources, and technological-economic decisions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The study highlights DeepSeek's impact on diagnostic accuracy, personalized treatment, medical documentation automation, and resource management optimization. Among the 17 surveyed hospitals, 6 hospitals employed detailed model versions, 5 used the 671B model, and 1 used the 32B version. Among 10 hospitals of different levels, 2 selected the 671B, 3 selected the 70B, and 4 selected the 32B model. All hospitals preferred local deployment. Different needs and applications were observed across the studied hospitals.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Selection of the right AI model requires balancing computational power with cost. Larger models offer higher accuracy, but incur higher costs, whereas distilled models suit smaller hospitals with fewer resources. Future development should therefore focus on selecting deployment strategies based on the hospital size while addressing data quality disparities to bridge the regional healthcare gaps. As such, coordination among government, hospitals, and doctors is crucial for supporting smarter healthcare transitions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"330-340"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized Precision in Healthcare Payment Systems: Insights From a Real-World Cancer Patients Cohort Study","authors":"Kan Xue,&nbsp;Hongrui Tian,&nbsp;Qi Wang,&nbsp;Yinkui Wang,&nbsp;Fei Shan,&nbsp;Yansui Yang,&nbsp;Miao Yu,&nbsp;Zhonghu He,&nbsp;Ziyu Li,&nbsp;Jiafu Ji","doi":"10.1002/hcs2.70078","DOIUrl":"10.1002/hcs2.70078","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The medical expenses of cancer patients have been rising worldwide, and the current payment system based on diagnosis-related groups is limited in reflecting the actual burden at the patient level. We aimed to develop a prediction model for reasonably high cost of hospitalization, taking gastric cancer (GC) patients undergoing surgery as an example.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this real-world patient cohort study, we collected clinical data from 3981 GC surgery inpatients in a tertiary hospital in Beijing, China. Reasonably high cost was defined as exceeding the 75th percentile of the hospitalization costs. The final model for reasonably high cost was derived based on univariate and multivariate unconditional logistic regression, with predictors selected according to the Akaike information criterion. Model performance was evaluated in terms of discrimination and calibration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The final model consisted of 13 predictors, with the area under the receiver operating characteristics curve of 0.718 (95% confidence interval: 0.700–0.736). Leave-one-out cross-validation generated an area under the receiver operating characteristics curve of 0.708 (95% confidence interval: 0.690–0.727). No significant differences in model discrimination were detected among the subgroups stratified by gender, age, N stage, surgical method, and admission year. The calibration plot showed good agreement between predicted probability and observed frequency of reasonably high cost.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our prediction tool for reasonably high cost of GC surgery patients has demonstrated good performance. The combined use of diagnosis-related groups payment and reasonably high cost assessment is expected to promote individualization and precision in medical cost control and ensure rational, efficient, and equitable provision of medical services.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"341-352"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and External Validation of an Efficient Deep Learning Model for Lesion Segmentation and Subtyping of Hepatic Echinococcosis in Ultrasound Imaging","authors":"Zhu He,&nbsp;Jiajun Qiu,&nbsp;Chenlin Du,&nbsp;Jin Yin,&nbsp;Xuhui Zhang,&nbsp;Yelei Ren,&nbsp;Yifei Wang,&nbsp;Lamu Suolang,&nbsp;Chunyang Li,&nbsp;Zongjiu Zhang,&nbsp;Diming Cai,&nbsp;Qicheng Lao","doi":"10.1002/hcs2.70077","DOIUrl":"10.1002/hcs2.70077","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Hepatic echinococcosis is a zoonotic parasitic disease common in remote, resource-limited pastoral regions. It mainly appears as cystic echinococcosis (CE), due to Echinococcus granulosus, and alveolar echinococcosis (AE), caused by multilocular Echinococcus species. CE can lead to biliary colic or vascular compression, while AE often mimics liver cancer and exhibits high long-term mortality. Although ultrasonography is the diagnostic method of choice, its accuracy is affected by imaging artifacts and operator variability. Conventional deep learning models are often computationally heavy and lack interpretability, limiting their use in underdeveloped areas. Integrating frequency- and spatial-domain features in a lightweight framework offers a promising solution for efficient segmentation and classification of hepatic echinococcosis. The aim of this study is to develop efficient deep learning models for hepatic echinococcosis segmentation and classification, facilitating large-scale screening with non-invasive, portable ultrasound imaging.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study utilized a large ultrasound dataset to train and evaluate a deep learning model, consisting of 20,112 images from 4437 patients in Shiqu County, Sichuan Province, China, an endemic area for hepatic echinococcosis. To further assess the model's robustness, an external dataset comprising 3340 images from 1123 patients at West China Hospital of Sichuan University was used for additional testing. By enhancing the correlation of image features in both the frequency and spatial domains for hepatic echinococcosis ultrasound images, and incorporating segmentation features to assist the classification task, the developed model achieves high efficiency and lightness.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The proposed model achieved a Dice coefficient of 80.67% and 78.12% for segmentation, and classification accuracy of 90.10% and 80.96% on the internal and external test sets, respectively. Compared to the lightweight state-of-the-art (SOTA) model, it improves inference speed by 43.48% and increases classification accuracy by 8.89% and 16.32% on internal and external test sets, respectively. Compared to the standard SOTA model, it is only 8% of its size but boosts inference speed by 821.37%, with classification accuracy improvements of 3.65% and 4.57% on internal and external test sets, respectively.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The proposed model offers efficient and accurate hepatic echinococcosis d","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"284-298"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13399102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pharmacogenomics-Guided Antihypertensive Strategy for Blood Pressure Reduction in Rural China: A Cluster-Randomized Controlled Trial","authors":"Wangjun Qin,&nbsp;Mengke Yu,&nbsp;Ying Zhu,&nbsp;Queran Lin,&nbsp;Limei Yin,&nbsp;Guisheng Liu,&nbsp;Yaping Wang,&nbsp;Guangqiang Lu,&nbsp;Xiaofei Liu,&nbsp;Pengmei Li,&nbsp;Xudong Kong,&nbsp;Li Zhao,&nbsp;Kun Tang,&nbsp;Jiangli Jin,&nbsp;Yu Xiong,&nbsp;Yingnan Zhao,&nbsp;Yan Yan,&nbsp;Bo Li,&nbsp;Azeem Majeed,&nbsp;Xianbo Zuo,&nbsp;Yue Qiu,&nbsp;Jiantuo Yu,&nbsp;Lihong Liu","doi":"10.1002/hcs2.70084","DOIUrl":"10.1002/hcs2.70084","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Although pharmacogenomics can effectively guide antihypertensive treatment, relevant evidence from low‑resource primary care settings is lacking. The aim of this study is to evaluate a pharmacogenomic‑guided antihypertensive strategy in real‑world, low‑resource primary care settings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We randomly assigned 94 villages (in 1:1:1 ratio, with stratification by villages) to receive a multifaceted intervention that included follow-up, health education, and medication adjustment guided by pharmacogenomics (treatment) or physician's experience (control), or routine care (observation) over 4 weeks. The primary outcome was the proportion of patients with controlled blood pressure after 4 weeks of treatment. Generalized linear, linear mixed-effect regression, and sensitivity analysis were conducted to assess the difference among groups. The intracluster correlation coefficient was calculated to assess the heterogeneity with cluster.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>This study included 1031 hypertensive patients from 90 villages of Dajie and Jiuzhi towns in Daming County from May 10 to December 31, 2022. Overall, 79 loci on 39 antihypertensive-related genes were tested for patients in the treatment group. After 4 weeks of treatment, 371 of 377 (98.4%) patients in the treatment group, 256 of 345 (74.2%) patients in the control group, and 232 of 309 (75.1%) patients in the observation group had their blood pressure under control. The between-group net differences were 24.2% (95% confidence interval [CI]: 19.4%–29.0%) and 23.3% (95% CI: 18.3%–28.3%) for the treatment versus control groups and treatment versus observation groups, respectively. After 4 weeks of treatment, the proportion of patients with overall incident adverse events was no different among the three groups (all <i>p</i> &gt; 0.05), and no serious adverse events or deaths related to antihypertensive treatment were reported during the study period.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A pharmacogenomic-guided antihypertensive strategy can significantly enhance blood pressure control while maintaining a similar safety level compared to an experience-guided antihypertensive strategy in low-resource, primary care settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"353-363"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beijing Roundtable Addresses Research Integrity's Role in Advancing Open Science","authors":"Wenqiong Lai,&nbsp;You Wu,&nbsp;Yu Sun","doi":"10.1002/hcs2.70089","DOIUrl":"10.1002/hcs2.70089","url":null,"abstract":"&lt;p&gt;On 17 June, a roundtable themed “Upholding Research Integrity to Advance Open Science” was convened at the 32&lt;sup&gt;nd&lt;/sup&gt; Beijing International Book Fair, bringing together experts from China and abroad. Six representatives from the International Association of Scientific, Technical &amp; Medical Publishers (STM), Taylor &amp; Francis, Tsinghua University, and Tsinghua University Press explored the emerging challenges that academic publishers and researchers face in the open science era, and proposed potential steps to strengthen research integrity.&lt;/p&gt;&lt;p&gt;The panel included Caroline Sutton, Chief Executive Officer of STM; Hylke Koers, Chief Information Officer, STM Solutions; Joris van Rossum, Program &amp; Product Director, STM Solutions; Katie Peace, Vice President of Academic Partnerships, Asia, at Taylor &amp; Francis; Xin Zhao, President of Tsinghua University Press; and You Wu, Associate Professor at the School of Healthcare Management, Tsinghua University.&lt;/p&gt;&lt;p&gt;Before the roundtable, Dr. Hylke Koers, Chief Information Officer of STM, introduced &lt;i&gt;STM Trends 2030&lt;/i&gt; [&lt;span&gt;1&lt;/span&gt;]. The report used space exploration as a metaphor for the pursuit of knowledge. He elaborated on the emerging trends poised to reshape scholarly communication over the next 5 years, identifying four major “solar flares”: geopolitical realignment, challenges to scientific authority, a funding shift from government to corporate research and development, and the artificial intelligence (AI)-driven transformation of knowledge production and dissemination. He also sounded a warning about “meteor” threats that demand attention—paper mills, predatory publishing, and mounting operational costs. Amid such turbulence, Dr. Koers urged publishers to center their efforts on two priorities: building knowledge infrastructure that enables human-machine collaboration, and fortifying authentication and trust mechanisms that safeguard research integrity.&lt;/p&gt;&lt;p&gt;The discussion that followed focused on four connected themes.&lt;/p&gt;&lt;p&gt;The experts explored methods, tools, and theoretical perspectives for promoting research integrity. AI has made data fabrication, image manipulation, and false manuscript production easier and cheaper. In some cases, an editor may receive a complete manuscript without being able to confirm whether the authors, images, or data are genuine. The panel therefore argued that the research community should not rely only on detecting misconduct after it has occurred. Greater attention may be given to verifying authenticity from the beginning of the research process [&lt;span&gt;2&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;To counter this, Dr. Van Rossum advocated for the adoption of technical safeguards—including image watermarks and researcher identity verification—to enable editors to verify the authenticity of images and the institutional affiliation of authors. He further emphasized the importance of enhanced collaboration across publishers, libraries, research institutions, funders, and ","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"266-268"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148674476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association Between eHealth Literacy and Online Health Information Seeking Behavior Among Cancer Patients: A Chain Mediating Role of Self-Efficacy and Information Utility","authors":"Tianchun Zhou,&nbsp;Mengmeng Lyu,&nbsp;Lu Chen,&nbsp;Mengting Ji","doi":"10.1002/hcs2.70079","DOIUrl":"10.1002/hcs2.70079","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Searching for health information online helps cancer patients better understand their health conditions and the way to cope with the adverse effects of treatment. Although eHealth literacy, self-efficacy and information utility are recognized as key factors that are associated with online health information seeking behavior (OHISB), the underlying mechanisms linking these factors in cancer patients remain understudied. This study aims to examine the mediating roles of self-efficacy and information utility in the relationship between eHealth literacy and OHISB.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this cross-sectional study, 502 cancer patients were selected from a tertiary hospital in Shanghai from June to December 2024. Demographic and clinical characteristics were obtained from electronic health records. eHealth literacy, self-efficacy, information utility and OHISB were assessed using self-report questionnaires. Mediation analysis was performed using structural equation modeling, with indirect effects evaluated through bootstrapping.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The prevalence of OHISB among cancer patients was 61.4%. There was a significant correlation between eHealth literacy, self-efficacy, information utility and OHISB (<i>r</i> = 0.357, 0.499, 0.469, <i>p</i> &lt; 0.01). eHealth literacy demonstrated both direct (<i>β</i> = 0.190, standard error [SE] = 0.050, 95% confidence interval [CI] [0.099, 0.295]) and indirect (<i>β</i> = 0.311, SE = 0.045, 95% CI [0.227, 0.404]) impacts on OHISB, mediated by self-efficacy (<i>β</i> = 0.223, SE = 0.038, 95% CI [0.155, 0.304]) and information utility (<i>β</i> = 0.056, SE = 0.025, 95% CI [0.013, 0.110]). The findings indicated that self-efficacy and information utility served as a sequential mediating factor in the association between eHealth literacy and OHISB, contributing 6.6% to the overall effect (<i>β</i> = 0.033, SE = 0.012, 95% CI [0.010, 0.057]).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>eHealth literacy enhances the OHISB of cancer patients through both direct effects and a chain mediation pathway involving self-efficacy and information utility. Targeted interventions should focus on improving eHealth literacy, strengthening self-efficacy, and developing reliable information platforms to optimize patient-centered health information services.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"299-309"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13399595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Methods to Predict the Length of Stay for Acute Stroke: A Scoping Review and Meta-Analysis","authors":"Zhenran Xu,&nbsp;Monique F. Kilkenny,&nbsp;Tzu-Yung Kuo,&nbsp;Jia Rong,&nbsp;Lachlan L. Dalli","doi":"10.1002/hcs2.70083","DOIUrl":"10.1002/hcs2.70083","url":null,"abstract":"<p>Reliable hospital length of stay (LOS) predictions improve patient outcomes and optimize clinical pathways in stroke care. There is limited evidence systematically investigating benefits and limitations of existing machine learning (ML) models for determining factors associated with LOS. We conducted a scoping review to: (1) investigate factors affecting LOS in stroke to identify key predictors for enhancing ML models; and (2) assess the performance of predictive models (ML and traditional models) to generate insights for improving LOS predictions. This review was undertaken in Ovid MEDLINE, Ovid Embase, and Scopus, searching English-language records from 2014 to 2024 using the search terms “stroke” and “length of stay”. We included studies investigating associated factors or assessing modeling approaches for predicting LOS in stroke. A meta-analysis using Bayesian methods was conducted to compare predictive performance (e.g., pooled <i>C</i>-statistics) of ML and logistic regression. Of 38 included studies, we identified 38 factors consistently extending LOS (patient characteristics [<i>n</i> = 20], health outcomes [<i>n</i> = 9], social circumstances [<i>n</i> = 2], and clinical care processes [<i>n</i> = 7]). Nine studies (24%) reported predictive models for predicting LOS (continuous or binary outcomes), with stroke severity (particularly the National Institutes of Health Stroke Scale at admission) being the most important predictor across seven studies. For binary outcomes, pooled <i>C</i>-statistic with 95% credible intervals was 0.764 (0.715–0.812) for best-performing ML models and 0.743 (0.692–0.795) for logistic regression across studies (no statistical difference; Bayes factor = 0.783). ML models did not demonstrate superior predictive performance, clinical reliability, and clinical utility over traditional models. Future ML models should prioritize a data-driven selection and transformation of informative inputs, enhance model generalizability and calibration, improve clinical interpretability, and implement a comprehensive model evaluation (including discrimination, calibration, and reclassification metrics). These methodological improvements enable more clinically meaningful LOS predictions to optimize personalized clinical pathways and support precise clinical decision-making at an early stage of stroke care.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"5 4","pages":"376-387"},"PeriodicalIF":6.0,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}