Most biomedical, health and care research does not adequately account for sex and gender dimensions of health and illness. Overlooking and disregarding the influence of sex and gender in research reduces scientific rigour and reproducibility, which leads to less effective treatments and worse health outcomes for all, particularly women and sex and gender diverse people. Historically, there has been minimal sex and gender policy innovation in UK medical research. To address this, stakeholders from across the UK research sector have been collaborating since spring 2023 to co-design a sex and gender policy framework to be implemented by research funders, as part of the MESSAGE (Medical Science Sex and Gender Equity) project. In the first Policy Lab, held in London in May 2023, 50 participants, including representatives from funding organisations, medical journals, regulators, clinicians, academics and people with lived experience, identified two key priorities for future action: 1) A whole system approach to policy change, and 2) Technical capacity-building and wider culture change efforts. In pursuing these priorities and collaborating cross-sectorally, UK stakeholders are engaged in an internationally innovative approach aimed at realising sustainable and impactful sex and gender policy change. Drawing on MESSAGE Policy Lab discussions, we set out key actions needed for the UK research sector to embed meaningful accounting for sex and gender as a new norm for research practice.

暂无摘要。

暂无摘要。

UNASSIGNED: Human rights provide a universal foundation for pursuing justice in public health in order to achieve the dignity of all individuals. In spite of international attempts to promote human rights in the context of health, a significant portion of India\'s indigenous population continues to have a limited understanding of these rights.
UNASSIGNED: This study aims to analyze tribal people\'s attitudes towards human rights in health care. The population consists of tribal residents from Manbazar - I and Puncha Blocks in the Puruliya district of West Bengal, India. Tribal young adults between the ages of 18 and 35 were the subject of a cross-sectional study.
UNASSIGNED: A pretested questionnaire was used to collect data. MS Excel and SPSS 27 were used for analysis. A descriptive analysis was carried out.
UNASSIGNED: The participants\' mean scores for awareness, accessibility and communication, autonomy and sexual and reproductive health and sexual and reproductive health rights (SRHR) were 8.06, 15.76, 7.35 and 32.52 revealing a moderate perception level among the young adult tribal population in the selected blocks.
UNASSIGNED: A holistic focus of the governmental and other non-governmental organizations towards the tribals is required. Introducing various aspects of human rights in healthcare in the education curriculum along with community outreach would by all likelihood improve the perception of \'Human Rights\' and thus help in better utilization of various services including health among tribal populations in India.

Background: In recent years, the integration of mobile health (m-Health) interventions has garnered increasing attention as a potential means to improve blood pressure (BP) management in adults. This updated systematic review with meta-analysis aimed to identify the effect of m-Health-based interventions on BP in adults and to evaluate the effect of m-Health on BP according to the characteristics of subjects, interventions, and countries. Methods: The search was carried out in PubMed, Embase, ResearchGate, and Cochrane databases in January 2022. Study selection and data extraction were performed by two independent reviewers. For analysis, random effects models were used with a confidence interval (CI) of 95% and p < 0.05. Results: Fifty studies were included in this review and in the meta-analysis. Interventions with m-Health reduced systolic BP in 3.5 mmHg (95% CI -4.3; -2.7; p < 0.001; I2 = 85.8%) and diastolic BP in 1.8 mmHg (95% CI -2.3; -1.4; p < 0.001; I2 = 78.9%) compared to usual care. The effects of m-Health interventions on BP were more evident in men and in older adults, in interventions lasting 6-8 weeks, with medication reminders, with the possibility of insertion of BP values (p < 0.05). Conclusion: The results of this study support the effectiveness of m-Health in reducing BP when compared to standard care. However, these effects are dependent on the characteristics of the subjects and interventions. Given the substantial heterogeneity among the results of this systematic review with meta-analysis, its interpretation should be cautious. Future research on this topic is warranted.

UNASSIGNED: Vasoplegia after cardiac surgery is associated with adverse outcomes. However, the clinical effects of vasoplegia and the significance of its duration after continuous-flow left ventricular assist device (CF-LVAD) implantation are less known.
UNASSIGNED: This study aimed to identify predictors of and outcomes from transient vs prolonged vasoplegia after CF-LVAD implantation.
UNASSIGNED: The study was a retrospective review of consecutive patients who underwent CF-LVAD implantation between January 1, 2005, and December 31, 2017. Vasoplegia was defined as the presence of all of the following: mean arterial pressure ≤65 mm Hg, vasopressor (epinephrine, norepinephrine, vasopressin, or dopamine) use for >6 hours within the first 24 hours postoperatively, cardiac index ≥2.2 L/min/m2 and systemic vascular resistance <800 dyne/s/cm5, and vasodilatory shock not attributable to other causes. Prolonged vasoplegia was defined as that lasting 12 to 24 hours; transient vasoplegia was that lasting 6 to <12 hours. Patient characteristics, outcomes, and risk factors were analyzed.
UNASSIGNED: Of the 600 patients who underwent CF-LVAD implantation during the study period, 182 (30.3%) developed vasoplegia. Mean patient age was similar between the vasoplegia and no-vasoplegia groups. Prolonged vasoplegia (n = 78; 13.0%), compared with transient vasoplegia (n = 104; 17.3%), was associated with greater 30-day mortality (16.7% vs 5.8%; P = 0.02). Risk factors for prolonged vasoplegia included preoperative dialysis and elevated body mass index.
UNASSIGNED: Compared with vasoplegia overall, prolonged vasoplegia was associated with worse survival after CF-LVAD implantation. Treatment to avoid or minimize progression to prolonged vasoplegia may be warranted.

Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre-training. In the last 3 years, these domain-specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs.

暂无摘要。

The Nordic countries are, together with the United States, forerunners in online record access (ORA), which has now become widespread. The importance of accessible and structured health data has also been highlighted by policy makers internationally. To ensure the full realization of ORA\'s potential in the short and long term, there is a pressing need to study ORA from a cross-disciplinary, clinical, humanistic, and social sciences perspective that looks beyond strictly technical aspects. In this viewpoint paper, we explore the policy changes in the European Health Data Space (EHDS) proposal to advance ORA across the European Union, informed by our research in a Nordic-led project that carries out the first of its kind, large-scale international investigation of patients\' ORA-NORDeHEALTH (Nordic eHealth for Patients: Benchmarking and Developing for the Future). We argue that the EHDS proposal will pave the way for patients to access and control third-party access to their electronic health records. In our analysis of the proposal, we have identified five key principles for ORA: (1) the right to access, (2) proxy access, (3) patient input of their own data, (4) error and omission rectification, and (5) access control. ORA implementation today is fragmented throughout Europe, and the EHDS proposal aims to ensure all European citizens have equal online access to their health data. However, we argue that in order to implement the EHDS, we need more research evidence on the key ORA principles we have identified in our analysis. Results from the NORDeHEALTH project provide some of that evidence, but we have also identified important knowledge gaps that still need further exploration.

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.