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Background Evidence for the relationship between maternal and perinatal factors and the success of vaginal birth after cesarean section (VBAC) is conflicting. We aimed to systematically analyze published data on maternal and fetal factors for successful VBAC. Methods A comprehensive search of Medline, Embase, and the Cumulative Index to Nursing and Allied Health Literature, from each database’s inception to March 16, 2018. Observational studies, identifying women with a trial of labor after one previous low-transverse cesarean section were included. Two reviewers independently abstracted the data. Meta-analysis was performed using the random-effects model. Risk of bias was assessed by the Newcastle-Ottawa Scale. Results We included 94 eligible observational studies (239,006 pregnant women with 163,502 VBAC). Factors were associated with successful VBAC with the following odds ratios (OR;95%CI): age (0.92;0.86–0.98), obesity (0.50;0.39–0.64), diabetes (0.50;0.42–0.60), hypertensive disorders complicating pregnancy (HDCP) (0.54;0.44–0.67), Bishop score (3.77;2.17–6.53), labor induction (0.58;0.50–0.67), macrosomia (0.56;0.50–0.64), white race (1.39;1.26–1.54), previous vaginal birth before cesarean section (3.14;2.62–3.77), previous VBAC (4.71;4.33–5.12), the indications for the previous cesarean section (cephalopelvic disproportion (0.54;0.36–0.80), dystocia or failure to progress (0.54;0.41–0.70), failed induction (0.56;0.37–0.85), and fetal malpresentation (1.66;1.38–2.01)). Adjusted ORs were similar. Conclusions Diabetes, HDCP, Bishop score, labor induction, macrosomia, age, obesity, previous vaginal birth, and the indications for the previous CS should be considered as the factors affecting the success of VBAC.

Artificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers. This review aimed to systematically examine the literature, in particular, focusing on the performance comparison between advanced AI and human clinicians to provide an up-to-date summary regarding the extent of the application of AI to disease diagnoses. By doing so, this review discussed the relationship between the current advanced AI development and clinicians with respect to disease diagnosis and thus therapeutic development in the long run. We systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered. A total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience. Current AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians' experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.

Objective. The reported incidence of type 2 diabetes mellitus (T2DM) after gestational diabetes (GDM) varies widely. The purpose of this meta-analysis was to define the incidence rate of T2DM among women with a history of GDM and to examine what might modulate the rate. Research Design and Methods. We searched PubMed and Embase for terms related to T2DM after GDM up to January 2019. Large cohort studies with sample size ≥300 and follow-up duration of at least one year were included. Data from selected studies were extracted, and meta-analysis was performed using the random-effects model. Subgroups analyses were based on the sample size of gestational diabetes, geographic region, maternal age, body-mass index, diagnostic criteria, and duration of follow-up. Results. Twenty-eight studies involving 170,139 women with GDM and 34,627 incident cases of T2DM were identified. The pooled incidence of T2DM after GDM was 26.20 (95% CI, 23.31 to 29.10) per 1000 person-years. Women from Asia and those with older age and higher body mass index seem to experience higher risk of developing T2DM. The incidence rate of T2DM was lowest when applying IADPSG (7.16 per 1000 person-years) to diagnose GDM. The risk of developing T2DM after GDM increased linearly with the duration of follow-up. The increments per year of follow-up were estimated at 9.6‰. The estimated risks for T2DM were 19.72% at 10 years, 29.36% at 20 years, 39.00% at 30 years, 48.64% at 40 years, and 58.27% at 50 years, respectively. Conclusions. The findings of very high incidence of T2DM after GDM add an important insight into the trajectory of the development of T2DM in the long-term postpartum periods, which could provide evidence for consultant and might motivate more women with GDM to screen for T2DM. This trial is registered with PROSPERO identifier CRD42019128980.

Background Health literacy is closely related to health status. Measuring public health literacy levels helps to warn of health status and manage health problems through timely interventions. The items of relevant evaluation tools are complex and numerous in China, and there is no recognized health literacy brief scale for the whole population. To translate the 12-item short-form health literacy scale (HLS-SF12) and test the validity and reliability of the Chinese version of the HLS-SF12 in the Chinese population. Methods The HLS-SF12 was translated into Chinese using the procedures of translation, back translation, and cultural debugging. 10,951 residents were selected by quota sampling method to test the validity and reliability of the scale, and 33 people were selected to retest after 2 weeks. The reliability was tested by using internal consistency coefficient and test-retest reliability. The validity was tested by using confirmatory factor analysis, content validity, convergent validity and discriminant validity. Results The Cronbach’s Alpha coefficient for the total scale was 0.94, and the test-retest reliability was 0.89. The Cronbach’s Alpha coefficients for the three subscales of health care, disease prevention, and health promotion respectively were 0.86, 0.86, 0.87, and the test-retest reliability respectively were 0.91, 0.79, 0.63. The confirmatory factor analysis identified a three factors model and showed nice goodness of fit indices for Chinese HLS-SF12 (GFI = 0.96, CFI = 0.97, IFI = 0.97, TLI = 0.96, and RMSEA = 0.07). Conclusion The Chinese version of the HLS-SF12 has good reliability and validity, and can be used as a tool to evaluate the health literacy of Chinese people.

Background The eating behaviors of older adults are associated with multiple factors. To promote older adults’ healthy diets, it is imperative to comprehensively study the factors associated with the eating behaviors of the aging population group. This study aimed to probe the associated factors of older adults’ eating behaviors from a socioecological model (SEM) perspective. Methods In 2021, a cross-sectional survey was performed to recruit participants in China. The survey data were analyzed using a multivariate generalized linear model to identify the factors associated with eating behaviors in older adults. Standardized regression coefficients (β) and 95% confidence intervals (CIs) were estimated using a multivariate generalized linear model. Results The survey contained 1147 valid older adult participants. Multivariate generalized linear model results showed that older adults with older age [aged 71–80 (β = -0.61), ≥ 81 (β = -1.12)], conscientiousness personality trait (β = -0.27), and higher family health levels (β = -0.23) were inclined to have better eating behaviors. The older adults with higher education levels [junior high school and high school (β = 1.03), junior college and above (β = 1.71)], higher general self-efficacy (β = 0.09), more severe depression symptoms (β = 0.24), and employment (β = 0.82) tended to have poorer eating behaviors. Conclusions This study identified factors that are specifically associated with older adults’ eating behaviors from an SEM perspective. The comprehensive multiple-angle perspective consideration may be a valuable idea for studying healthy eating behaviors in older adults.

People across the world have been greatly affected by the ongoing coronavirus disease (COVID-19) pandemic. The high infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospitals is particularly problematic for recently delivered mothers and currently pregnant women who require professional antenatal care. Online antenatal care would be a preferable alternative for these women since it can provide pregnancy-related information and remote clinic consultations. In addition, online antenatal care may help to provide relatively economical medical services and diminish health care inequality due to its convenience and cost-effectiveness, especially in developing countries or regions. However, some pregnant women will doubt the reliability of such online information. Therefore, it is important to ensure the quality and safety of online services and establish a stable, mutual trust between the pregnant women, the obstetric care providers and the technology vis-a-vis the online programs. Here, we report how the COVID-19 pandemic brings not only opportunities for the development and popularization of online antenatal care programs but also challenges.

Gout is a common and debilitating condition that is associated with significant morbidity and mortality. Despite advances in medical treatment, the global burden of gout continues to increase, particularly in high-sociodemographic index (SDI) regions. To address the aforementioned issue, we used age-period-cohort (APC) modeling to analyze global trends in gout incidence and prevalence from 1990 to 2019. Data were extracted from the Global Burden of Disease Study 2019 to assess all-age prevalence and age-standardized prevalence rates, as well as years lived with disability rates, for 204 countries and territories. APC effects were also examined in relation to gout prevalence. Future burden prediction was carried out using the Nordpred APC prediction of future incidence cases and the Bayesian APC model. The global gout incidence has increased by 63.44% over the past 2 decades, with a corresponding increase of 51.12% in global years lived with disability. The sex ratio remained consistent at 3:1 (male to female), but the global gout incidence increased in both sexes over time. Notably, the prevalence and incidence of gout were the highest in high-SDI regions (95% uncertainty interval 14.19-20.62), with a growth rate of 94.3%. Gout prevalence increases steadily with age, and the prevalence increases rapidly in high-SDI quantiles for the period effect. Finally, the cohort effect showed that gout prevalence increases steadily, with the risk of morbidity increasing in younger birth cohorts. The prediction model suggests that the gout incidence rate will continue to increase globally. Our study provides important insights into the global burden of gout and highlights the need for effective management and prophylaxis of this condition. The APC model used in our analysis provides a novel approach to understanding the complex trends in gout prevalence and incidence, and our findings can inform the development of targeted interventions to address this growing health issue.

By the end of 2022, 1.223 million people were living with HIV in China. Beyond medical challenges, they often face stigma and social exclusion. In China, Sina Weibo (Sina Corporation), with over 582 million monthly active users as of 2022, has emerged as a critical space for people living with HIV, many of whom identify as \"A-friends.\" They navigated these complex dynamics of visibility. In this context, visibility, understood as both the capacity to be seen and the power relations it entails, is a central affordance of social media. This study aimed to explore how A-friends navigate their visibility on Weibo, focusing on the dual-edged nature of visibility. It examines how visibility can empower marginalized groups while also exposing them to risks. The study highlights the tension between these dynamics and aims to inform the creation of supportive digital environments that balance empowerment with protection from harm. We conducted nonparticipant observation and semistructured interviews with 30 A-friends, recruited through opportunistic and snowball sampling on social media platforms. The data were analyzed thematically using NVivo 11.0 (QSR International). Among the participants, 86.67% (26/30) were interviewed via internet-based voice chat, 10% (3/30) offline, and 3.33% (1/30) by text. To confirm theoretical saturation, 3 additional interviews were coded separately, yielding no new themes. As shown by the data, the majority of participants (56.67%, 17/30) were aged between 30 years and 40 years, with 43.33% (13/30) holding a bachelor's degree or higher. Most participants (46.67%, 14/30) were diagnosed with HIV 1-5 years ago, and all participants were asymptomatic. After coding the interviews, we identified 2 overarching themes during the development of the coding framework, each comprising 3 subcategories, resulting in a total of 6 subcategories. Theme 1 highlighted the positive implications of visibility, referred to as the Climb Effect, which included (1) self-reconstruction through illness narratives, (2) relational bonding and community building, and (3) public advocacy to challenge stigma. Theme 2 focused on the negative consequences, termed the Slide Effect, which encompassed (1) the reproduction of social exclusion and limited public empathy, (2) privacy concerns and risks of unintended disclosure, and (3) ego depletion. This study highlights the layered and cyclical nature of visibility in online health communities, which we conceptualize through a visibility ladder model. Self-visibility promotes personal growth, health self-management, and psychological resilience but also introduces risks of self-stigmatization and emotional exhaustion. Social visibility strengthens peer support and shared identity while exposing individuals to privacy breaches and misinformation. Public visibility empowers collective action and advocacy, yet is constrained by persistent societal stigma and platform algorithms that limit audience reach. Future efforts should prioritize enhancing eHealth literacy, strengthening privacy protections, and promoting inclusive, stigma-reducing digital environments to optimize the benefits of visibility and mitigate its potential harms.

Background With the release of the Health China Action (2019–2030), family health is receiving increasing attention from experts and scholars. But at present, there is no family health scale in China that involves multidimensional and interdisciplinary commonality. Aim To translate a Short Form of the Family Health Scale (FHS-SF) and to test the reliability and validity of the Chinese version of the FHS-SF. Method A Short Form of the Family Health Scale was Chinese translated with the consent of the original author. A total of 8912 residents were surveyed in 120 cities across China using a multistage sampling method, with gender, ethnicity, and education level as quota variables. Seven hundred fifty participants were selected to participate in this study, and 44 participants were randomly selected to be retested 1 month later. Results The Cronbach’s alpha of the Chinese version of a Short Form the Family Health Scale was 0.83,the Cronbach’s alphas of the four subscales ranged from 0.70 to 0.90, the retest reliability of the scale was 0.75, the standardized factor loadings of the validation factor analysis were above 0.50, GFI = 0.98; NFI = 0.97; RFI = 0.95; RMSEA = 0.07, all within acceptable limits. Conclusion The Chinese version of a Short Form the Family Health Scale has good reliability and validity and can be used to assess the level of family health of Chinese residents.

Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals. This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals. This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time. From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system-1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53). As the first of such type of studies, we provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.