Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos.

In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons' skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is profoundly reliant on large-scale datasets and annotations. In particular, surgical scene understanding and phase recognition stand as pivotal pillars within the realm of computer-assisted surgery and post-operative assessment of cataract surgery videos. In this context, we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis and detecting post-operative irregularities in cataract surgery. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures for phase recognition and surgical scene segmentation. Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos. The dataset and annotations are publicly available in Synapse.

Harnessing technology to improve sleep in frontline healthcare workers: A pilot study of electronic noise-masking earbuds on subjective and objective sleep measures.

Objective: This pilot study assessed the effects of electronic noise-masking earbuds on subjective sleep perception and objective sleep parameters among healthcare workers (HCWs) reporting sleep difficulties during the COVID-19 pandemic. Methods: Using a pre-post design, 77 HCWs underwent 3 nights of baseline assessment followed by a 7-night intervention period. Participants wore an at-home sleep monitoring headband to assess objective sleep measures and completed subjective self-report assessments. The difference in mean sleep measures from baseline to intervention was estimated in linear mixed models. Results: Compared to baseline assessments, HCWs reported significant improvements in sleep quality as measured by the Insomnia Severity Index (ISI) (Cohen's d = 1.74, p < 0.001) and a significant reduction in perceived sleep onset latency (SOL) during the intervention (M = 17.2 minutes, SD = 7.7) compared to baseline (M = 24.7 minutes, SD = 16.1), (Cohen's d = -0.42, p = 0.001). There were no significant changes in objective SOL (p = 0.703). However, there was a significant interaction between baseline objective SOL (<20 minutes vs >20 minutes) and condition (baseline vs intervention) (p = 0.002), such that individuals with objective SOL >20 minutes experienced a significant decrease in objective SOL during the intervention period compared to baseline (p = 0.015). Conclusions: HCWs experienced a significant improvement in perceived SOL and ISI scores after using the electronic noise-masking earbuds. Our data provide preliminary evidence for a nonpharmacological intervention to improve the sleep quality of HCWs which should be confirmed by future controlled studies.

Inflammatory bowel disease patients' perspectives of non-medical needs.

BACKGROUND: Inflammatory bowel disease (IBD) imposes a huge burden on the healthcare systems and greatly declines the patient's quality of life. However, there is a paucity of detailed data regarding information and supportive needs as well as sources and methods of obtaining information to control different aspects of the disease from the perspectives of the patients themselves. This study aimed to establish the IBD patients' preferences of informational and supportive needs through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). METHODS: IBD patients were recruited from different centers. Considering inclusion and exclusion criteria, 521 participants were filled a predefined questionnaire. This questionnaire was prepared through literature review of the recent well-known guidelines on the needs of IBD patients, which was further approved by the experts of IBD area in three rounds of Delphi consensus. It includes 56 items in four sections of informational needs (25), supportive needs (15), sources of information (7), and methods of obtaining information (9). RESULTS: In particular, EFA was used to apply data reduction and structure detection. Given that this study tries to identify patterns, structures as well as inter-relationships and classification of the variables, EFA was utilized to simplify presentation of the variables in a way that large amounts of observations transform into fewer ones. Accordingly, the EFA identified five factors out of 25 items in the information needs section, three factors out of 15 items in the supportive needs section, two factors out of 7 items in the information sources section, and two factors out of 9 items in the information presentation methods. Through the CFA, all 4 models were supported by Root Mean Squared Error of Approximation (RMSEA); Incremental Fit Index (IFI); Comparative Fit Index (CFI); Tucker-Lewis Index (TLI); and SRMR. These values were within acceptable ranges, indicating that the twelve factors achieved from EFA were validated. CONCLUSIONS: This study introduced a reliable 12-factor model as an efficient tool to comprehensively identify preferences of IBD patients in informational and supportive needs along with sources and methods of obtaining information. An in-depth understanding of the needs of IBD patients facilitates informing and supporting health service provision. It also assists patients in a fundamental way to improve adaptation and increase the quality of life. We suggest that health care providers consider the use of this tool in clinical settings in order to precisely assess its efficacy.

Acute spinal cord injury serum biomarkers in human and rat: a scoping systematic review.

STUDY DESIGN: Scoping systematic review. OBJECTIVES: To summarize the available experimental clinical and animal studies for the identification of all CSF and serum-derived biochemical markers in human and rat SCI models. SETTING: Tehran, Iran. METHODS: In this scoping article, we systematically reviewed the electronic databases of PubMed, Scopus, WOS, and CENTRAL to retrieve current literature assessing the levels of different biomarkers in human and rat SCI models. RESULTS: A total of 19,589 articles were retrieved and 6897 duplicated titles were removed. The remaining 12,692 studies were screened by their title/abstract and 12,636 were removed. The remaining 56 were considered for full-text assessment, and 11 papers did not meet the criteria, and finally, 45 studies were included. 26 studies were human observational studies comprising 1630 patients, and 19 articles studied SCI models in rats, including 832 rats. Upon reviewing the literature, we encountered a remarkable heterogeneity in terms of selected biomarkers, timing, and method of measurement, studied models, extent, and mechanism of injury as well as outcome assessment measures. CONCLUSIONS: The specific expression and distribution patterns of biomarkers in relation to spinal cord injury (SCI) phases, and their varied concentrations over time, suggest that cerebrospinal fluid (CSF) and blood biomarkers are effective measures for assessing the severity of SCI.

A noise audit of human-labeled benchmarks for machine commonsense reasoning.

With the advent of large language models, evaluating and benchmarking these systems on important AI problems has taken on newfound importance. Such benchmarking typically involves comparing the predictions of a system against human labels (or a single 'ground-truth'). However, much recent work in psychology has suggested that most tasks involving significant human judgment can have non-trivial degrees of noise. In his book, Kahneman suggests that noise may be a much more significant component of inaccuracy compared to bias, which has been studied more extensively in the AI community. This article proposes a detailed noise audit of human-labeled benchmarks in machine commonsense reasoning, an important current area of AI research. We conduct noise audits under two important experimental conditions: one in a smaller-scale but higher-quality labeling setting, and another in a larger-scale, more realistic online crowdsourced setting. Using Kahneman's framework of noise, our results consistently show non-trivial amounts of level, pattern, and system noise, even in the higher-quality setting, with comparable results in the crowdsourced setting. We find that noise can significantly influence the performance estimates that we obtain of commonsense reasoning systems, even if the 'system' is a human; in some cases, by almost 10 percent. Labeling noise also affects performance estimates of systems like ChatGPT by more than 4 percent. Our results suggest that the default practice in the AI community of assuming and using a 'single' ground-truth, even on problems requiring seemingly straightforward human judgment, may warrant empirical and methodological re-visiting.

Artificial intelligence-based prescription of personalized scalp cosmetics improved the scalp condition: efficacy results from 100 participants.

Background: Scalp-related symptoms such as dandruff and itching are common with diverse underlying etiologies. We previously proposed a novel classification and scoring system for scalp conditions, called the scalp photographic index (SPI); it grades five scalp features using trichoscopic images with good reliability. However, it requires trained evaluators.Aim: To develop artificial intelligence (AI) algorithms for assessment of scalp conditions and to assess the feasibility of AI-based recommendations on personalized scalp cosmetics.Methods: Using EfficientNet, convolutional neural network (CNN) models (SPI-AI) ofeach scalp feature were established. 101,027 magnified scalp images graded according to the SPI scoring were used for training, validation, and testing the model Adults with scalp discomfort were prescribed shampoos and scalp serums personalized according to their SPI-AI-defined scalp types. Using the SPI, the scalp conditions were evaluated at baseline and at weeks 4, 8, and 12 of treatment.Results: The accuracies of the SPI-AI for dryness, oiliness, erythema, folliculitis, and dandruff were 91.3%, 90.5%, 89.6%, 87.3%, and 95.2%, respectively. Overall, 100 individuals completed the 4-week study; 43 of these participated in an extension study until week 12. The total SPI score decreased from 32.70 ± 7.40 at baseline to 15.97 ± 4.68 at week 4 (p < 0.001). The efficacy was maintained throughout 12 weeks.Conclusions: SPI-AI accurately assessed the scalp condition. AI-based prescription of tailored scalp cosmetics could significantly improve scalp health.

Awareness and Self-Reported Knowledge and Training on Nutrition in Older People among Primary Care Practitioners.

OBJECTIVES: To assess the awareness and training of primary care physicians on nutrition in older patients. DESIGN: Observational, real-world data survey. SETTING: Primary Care. PARTICIPANTS: One hundred sixty-two physicians, generalists and specialists, working in primary care. MEASUREMENTS: Participants received an online questionnaire with 18 questions concerning the importance of nutrition, degree of knowledge, needs, and training in nutrition. The results were evaluated using univariate descriptive analysis, with a percentage for each chosen answer. Logistic regression models were used to evaluate if answers were related to training in nutrition and professional experience. RESULTS: 43.2% of participants reported that nutrition is very important in individuals over 65 years old, and 90% were aware of the importance of nutrition in healthy aging. Nutritional problems affect 30 to 50% of patients, according to 44.7% of participants. 89.2% agree about the need for nutritional assessment in older patients; however, the professionals believe they should be better prepared. Two out of three respondents consider the training received in nutrition during their undergraduate course or continuing medical education as deficient. Time of professional practice was mainly associated with conceptual facts, while continuing medical education did with practical issues, mainly the use of screening and diagnostic tools [FRAIL (OR: 3.16; 95%IC: 1.55-6.46), MNA-SF (OR: 6.455; 95%IC: 2.980-13.981) and SARC-F (OR: 3.063; 95%IC: 1.284-7.309)]. CONCLUSION: Although primary care professionals are aware of the importance of nutrition in older patients, there are still gaps in daily practice that could be improved by developing educational strategies.

Exploring the Link Between Ankylosing Spondylitis and Inflammatory Bowel Disease: A Retrospective Cohort Study.

BACKGROUND: Ankylosing spondylitis (AS) and inflammatory bowel disease (IBD) are chronic conditions with overlapping pathogenic mechanisms. The genetic predisposition and inflammatory pathways common to both diseases suggest a syndemic relationship. While some evidence points to a connection between the two conditions, other reports do not support this link. OBJECTIVES: To investigate the association between AS and the subsequent incidence of IBD. To identify potential risk factors and effect modifiers that contribute to this relationship. METHODS: Utilizing the Chronic Disease Registry of Clalit Health Services, we conducted a retrospective cohort study of individuals diagnosed with AS between January 2002 and December 2018. We compared these patients with age- and sex-matched controls, excluding those with a prior diagnosis of IBD. Statistical analyses included chi-square and t-tests for demographic comparisons, and Cox proportional hazards models for evaluating the risk of IBD development, with adjustments for various co-morbidities and demographic factors. RESULTS: The study included 5825 AS patients and 28,356 controls. AS patients demonstrated a significantly higher incidence of IBD with hazard ratios of 6.09 for Crohn's disease and 2.31 for ulcerative colitis, after multivariate adjustment. The overall incidence of IBD in the AS cohort was significantly higher compared to controls. CONCLUSIONS: AS patients exhibit a markedly increased risk of developing IBD. These findings advocate for heightened clinical vigilance for IBD symptoms in AS patients and suggest the need for a multidisciplinary approach to patient care. Further research into the shared pathogenic pathways is needed to develop personalized treatment strategies and improve patient management.

Effects of disability on adverse health outcomes and anthropometric deficits among under-five children in South Asian countries: evidence from multiple indicator cluster surveys.

Background: Children with disabilities face an increased risk of adverse health outcomes and poor anthropometric deficits, although the focus on them is limited in the South Asian context thus far and need newer and more evidence. This study investigates the effects of disability on adverse health outcomes and anthropometric deficits among 2-4 years aged children in South Asian countries. Methods: We analyzed data from 93,180 children aged 2-4 years across Bangladesh, Nepal, Pakistan, and Afghanistan using Multiple Indicator Cluster Surveys (2017-2023). Disability status was the primary exposure, and outcomes included adverse health outcome (acute respiratory infection, diarrhea, fever), anthropometric deficit (stunting, wasting, underweight), and healthcare service sources during adverse health events (care received from skilled healthcare personnel, care received from non-professional personnel, and care received from health facility workers other than skilled healthcare personnel). Using multilevel and multinomial logistic regression models, we examined associations between exposure and outcome variables, adjusting for covariates. Findings: We found average disability prevalence in South Asia was 8.7% (8.3-9.0; n = 8072), varying from 3.4% (3.0-3.8; n = 446) in Bangladesh to 12.3% (11.4-13.3; n = 1259) in Afghanistan. Common health issues included fever (n = 24,982, 26.8%, 26.2-27.4) and diarrhea (n = 14,081, 15.1%, 14.7-15.6), while prevalent poor anthropometric deficits were stunting (n = 39,766, 42.7%, 42.0-43.3) and underweight (n = 22,390, 24.0%, 23.5-24.5). Children with disability had 1.30 (95% CI: 1.21-1.40) to 1.60 (95% CI: 1.47-1.75) times and 1.17 (95% CI: 1.05-1.29) to 1.39 (95% CI: 1.30-1.48) times higher likelihoods of adverse health outcomes and anthropometric deficits, respectively, with variations observed among countries and different disability types. Individuals with disability were 1.16 (95% CI: 1.00-1.35) to 1.26 (95% CI: 1.01-1.58) times more likely to receive healthcare services from skilled healthcare personnel compared to health facility workers other than skilled healthcare personnel. Interpretation: This study findings emphasizes the need for community-level awareness programs to improve anthropometric well-being and healthcare of the children with disability. Funding: This research did not receive any specific funds.

Hidradenitis suppurativa as a potential risk factor of periodontitis: a multi-center, propensity-score-matched cohort study.

Background: Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease associated with systemic symptoms. Periodontitis, a prevalent dental disease, shares immune-mediated inflammatory characteristics with HS. This cohort study aims to evaluate the association between HS and periodontitis. Methods: Using the TriNetX research network, a global-federated database of electronic health records, we conducted a retrospective cohort study. People being diagnosed of HS were identified and propensity score matching was performed to identify proper control group, via balancing critical covariates Within the follow-up time of 1 year, 3 year and 5 years, hazard ratios were calculated to assess the risk of periodontitis in HS patients compared to controls. Results: Within the 53,968 HS patients and the same number of matched controls, the HS patients exhibited a significantly increased risk of developing periodontitis compared to controls after 3 years of follow-up (HR: 1.64, 95% CI: 1.11, 2.44) and 5 years of follow-up (HR: 1.64, 95% CI: 1.21, 2.24) of follow-up. Sensitivity analyses supported these findings under various matching models and washout periods. While comparing with patients with psoriasis, the association between HS and periodontitis remained significant (HR: 1.73, 95% CI: 1.23, 2.44). Conclusion: The observed increased risk suggests the need for heightened awareness and potential interdisciplinary care for individuals with HS to address periodontal health.

Systematic review of mediterranean diet interventions in menopausal women.

The increasing lifespan of women and their extended time spent in menopause pose significant challenges for health care systems, primarily due to the impacts of postmenopausal estrogen deficiency and aging on health. Menopause's onset is linked to a heightened prevalence of obesity, metabolic syndrome, cardiovascular disease, and osteoporosis. Diet is particularly relevant during menopause given its impact on quality of life and longevity and its modifiability. Because the Mediterranean diet is currently regarded as one of the healthiest dietary models in the world, the aim of this systematic review was to assess current evidence regarding the effectiveness of studies on the Mediterranean diet as an intervention for menopausal women. A systematic review of intervention-based studies involving the Mediterranean diet among menopausal women was performed in Scopus, PubMed, and Web of Science. The results of seven that met the inclusion criteria suggests that adherence to the Mediterranean diet can have beneficial impacts on menopausal women's health, including reductions in weight, blood pressure, blood ω6: ω3 ratio, triglycerides, total cholesterol, and LDL levels. Those results seem to be relevant for public health interventions aimed at improving menopausal women's quality of life.

Mortality risk factors in the dependent population of Castilla-La Mancha (Spain) before and during the first COVID-19 wave.

The coronavirus disease pandemic has had an important impact worldwide. The population aged over 65 years and aged dependent persons are the population groups which have suffered in a highest level the consequences of the pandemic in terms of cases and death. In Spain, the situation is similar to other countries, but regional studies are needed because competencies on long-term care depend on regional public administration. Thus, the aim of this work is to analyse social and individual factors associated with the risk of mortality of legally recognised dependent people during the pandemic compared to a non-pandemic period. The data were extracted from the administrative database on individuals included in Castilla-La Mancha's long-term care system and it was merged with the information from the Spanish National Death Index administered by the Ministry of Health, Consumption and Social Welfare. The results show that the risk of mortality between March and June 2020 was positively associated with being male; being older than 65, with an especially high impact in the group aged over 90; having a higher level of dependency; living in a nursing home; and living in a place with more population density. Intraregional differences related to health areas also exists in both pandemic and non-pandemic periods. These findings are critical with a view to enhancing protocols for the care of the most vulnerable population groups.

After-meal blood glucose level prediction for type-2 diabetic patients.

Type 2 Diabetes, a metabolic disorder disease, is becoming a fast growing health crisis worldwide. It reduces the quality of life, and increases mortality and health care costs unless managed well. After-meal blood glucose level measure is considered as one of the most fundamental and well-recognized steps in managing Type 2 diabetes as it guides a user to make better plans of their diet and thus control the diabetes well. In this paper, we propose a data-driven approach to predict the 2 h after meal blood glucose level from the previous discrete blood glucose readings, meal, exercise, medication, & profile information of Type 2 diabetes patients. To the best of our knowledge, this is the first attempt to use discrete blood glucose readings for 2 h after meal blood glucose level prediction using data-driven models. In this study, we have collected data from five prediabetic and diabetic patients in free living conditions for six months. We have presented comparative experimental study using different popular machine learning models including support vector regression, random forest, and extreme gradient boosting, and two deep layer techniques: multilayer perceptron, and convolutional neural network. We present also the impact of different features in blood glucose level prediction, where we observe that meal has some modest and medication has a good influence on blood glucose level.

Communities of Care Approach: Developing a Place-based Model of Care and Building Partnerships in the Communities in Central Singapore.

The population in Singapore is ageing, adding pressure to community care as the health and social needs of its residents increase. This has accelerated the pace at which Regional Health Systems adopt and deliver its population health strategies from early prevention, chronic disease management, crisis care to end-of-life care. To this end, the Central Health Integrated Care Network (ICN) began its journey to develop Communities of Care (CoCs) with other health and social care partners to meet the needs of residents in the Central Zone of Singapore. This paper describes the processes and steps taken by Central Health ICN to build partnerships with other agencies and organisations to build place-based models of care in the local neighbourhoods. The faciliating factors and the barriers faced in the implementation of CoCs were described to allow sharing of such learnings on large scale change. Strategies in overcoming some of the challenges were also presented to demonstrate the iterative processes required in building integrated place-based models of care to meet the needs of the residents in different communities.

Lessons Learned from the Implementation of Youth Wellness Hubs Ontario, an Integrated Youth Services Network: Perspectives from Network Leads.

Introduction: Mental health and substance use services for youth in Canada continue to be fragmented. In response, Integrated Youth Services (IYS) has been proposed to address gaps in youth mental health services that can lead to improved youth outcomes. Youth Wellness Hubs Ontario (YWHO) was launched in 2017 as Ontario's IYS Network for youth ages 12-25, prioritizing continuous improvement through evaluation. Description: At the end of the first three years of the YWHO initiative, an evaluation was carried out to identify the barriers and facilitators to the initial implementation of YWHO and service delivery modifications resulting from the COVID-19 pandemic across ten sites. Reporting on these is the focus of this article. Key informant interviews were conducted in early 2021 with Network Leads from all ten initial YWHO sites. Reflexive thematic analysis was used to analyze all interview data. Discussion: Facilitators to the implementation of the YWHO model included diversified funding models, YWHO Provincial Office implementation supports, clear hub processes, robust community partnerships, organizational support and dedicated staff. Common barriers included certain challenges related to staffing and finances, implementation of the shared data collection platform, implementation of measurement-based care, partnerships, integrated service delivery, and branding and communications. Conclusion: Implementation of IYS is highly collaborative and quite complex. As interest in such models increase, so does the need for knowledge related to optimal implementation. Learnings have informed developments and improvements made to the YWHO model. Insights will also inform how stakeholders support youth in their communities in designing and implementing services that improve youth mental health and overall well-being.

Association Between Early Hyponatremia and Clinical Outcomes in Critically Ill Patients: A Retrospective Cohort Study.

INTRODUCTION: Hyponatremia, frequently encountered in intensive care (ICU) settings, plays a critical role in shaping patient outcomes. Despite its prevalence, contemporary research into its newly classified severity categories and their implications on mortality, renal function, and length of stay remains limited. This study aims to fill this gap by examining the impact of hyponatremia severity on these critical outcomes. METHODS: A retrospective analysis of ICU patients aged >18 years who were admitted between March 2019 and December 2022 was conducted at Hamamatsu University Hospital, Shizuoka, Japan. Patients who were readmitted or had incomplete data were excluded. Hyponatremia was categorized as mild (130-135 mmol/L), moderate (125-129 mmol/L), or severe (<125 mmol/L), following the criteria set by the European Society of Intensive Care Medicine. This classification utilized the lowest sodium concentration within 24 hours of ICU admission. The outcomes were in-hospital mortality, ICU mortality, newly implemented renal replacement therapy (RRT), and length of hospital and ICU stay. Outcomes were analyzed using multivariable logistic and linear regression models, adjusting for relevant covariates including age, sex, Acute Physiology and Chronic Health Evaluation (APACHE) III scores, and the use of mechanical ventilation. RESULTS: Of the 3,538 patients analyzed, 1,072 (30.3%) experienced hyponatremia: 894 (25.3%) mild, 144 (4.1%) moderate, and 34 (1.0%) severe. Multivariable analysis revealed no significant association between hyponatremia severity and in-hospital mortality rates across normonatremia (3.8%), mild (5.2%), moderate (11.8%), and severe (23.5%) groups, nor with ICU mortality. However, compared to normonatremia, moderate and severe hyponatremia were associated with increased RRT initiation (odds ratios = 3.83 and 6.36, respectively) and prolonged hospital stay (mean difference = 7.06 and 9.66 days, respectively), and ICU stays (mean difference, 1.02 and 2.70 days, respectively). Mild hyponatremia was not significantly associated with RRT or length of stay. CONCLUSION: Moderate-to-severe hyponatremia did not influence mortality but was associated with increased RRT initiation and prolonged hospital and ICU stay. By contrast, mild hyponatremia was not associated with any clinical outcome. Further research is required to determine if correcting hyponatremia directly improves ICU patient outcomes, given the observational nature of the study.

The impact of social support on benefit finding among patients with advanced lung cancer and their caregivers: based on actor-partner interdependence mediation model.

PURPOSE: Advanced lung cancer and its treatment serve as a sudden stressful event that profoundly impacts the psychological experience of both the patients and their primary caregiver. This study used dyadic analyses to explore the dyadic effects of social support on benefit finding and whether hope level mediates the patient-caregiver dyads in advanced lung cancer. METHODS: Two hundred ninety-five pairs of patients with advanced lung cancer and primary caregivers completed the Social Support Rating Scale (SSRS), the Herth Hope Index (HHI), and the Benefit Finding Scale (BFS). Dyadic analyses were conducted using structural equation modelling based on the actor-partner interdependence mediation model. RESULTS: The results indicated that for both patients (B = 0.259, 95% CI = 0.135-0.423, P < 0.001) and their primary caregivers (B = 0.596, 95% CI = 0.403-0.838, P < 0.001), hope level mediated the actor effect of social support on benefit finding; social support was positively associated with hope level and further enhanced benefit finding. Regarding partner effects (B = 0.242, 95% CI = 0.119-0.404, P < 0.001), primary caregivers' social support significantly indirectly affected patients' benefit finding through patients' hope level. CONCLUSION: There is an interaction between social support, hope level, and benefit finding in patients with advanced lung cancer and their primary caregivers. Healthcare professionals ought to be vigilant in recognizing patients and caregivers who are vulnerable, have limited social support, and possess diminished hope levels. At the same time, nurses should provide timely psychological support and counseling to patients and their caregivers, encourage them to actively participate in social activities, and inspire their confidence and hope in life, thus improving their benefit findings.

The Development and Feasibility of a Novel Group Intervention to Support Helping Professionals in Metabolizing Suffering and Engaging Strengths: The CHRYSALIS Program.

This feasibility study reports on the development and initial evaluation of a novel online intervention for helping professionals (HPs; i.e. mental health professionals, chaplains, clergy) designed to (a) address occupational hazards, such as burnout and vicarious traumatization, and (b) promote well-being and flourishing at work. In contrast with competency and self-care focused models, the CHRYSALIS (Catalyzing Helping Professionals' Resilience, VitalitY, Spirituality, Authentic Living, and Inner Strength) intervention centers the self of the provider, explores cultural and spiritual contexts, and attends to systemic challenges. As part of a larger randomized controlled trial evaluating two program formats, the group format entails eight online sessions exploring strengths that can promote well-being, including processing, relational, vitalizing, orienting, and agentic capacities. To pilot test this framework and establish proof of concept, this study analyzed data from 41 HPs who had been randomly assigned to the group condition and completed surveys at four time points. Quantitative results indicated significant reductions in vicarious traumatization and burnout as well as increased well-being and meaning in work. Qualitative results suggest the intervention fostered relational support, cultivated new perspectives, and increased engagement with strengths, positively impacting participants' work and navigation of caregiving systems. Feedback about cohesion and group dynamic challenges in an online format informed further program development. This study provides initial support for the feasibility and efficacy of the group format of the CHRYSALIS intervention as a creative means to address HPs' risk for occupational hazards and promote holistic formation in a relational context.

Converting IMPROVE bleeding and VTE risk assessment models into a fast-and-frugal decision tree for VTE prophylaxis.

Current hospital VTE (venous thromboembolism) prophylaxis for medical patients has been characterized by both underuse and, increasingly, overuse. The American Society of Hematology (ASH) has endorsed the use of risk assessment models (RAM) as an approach to individualize VTE prophylaxis as a way of balancing overuse (excessive risk of bleeding) and underuse (risk of avoidable VTE). ASH has endorsed IMPROVE (International Medical Prevention Registry on Venous Thromboembolism) risk assessment models - the only RAM to assess short-term bleeding and VTE risk in acutely ill medical inpatients. ASH, however, notes that no RAMs have been thoroughly analyzed for their effect on patient outcomes. We aimed to validate the IMPROVE models and adapt them into a simple, fast-and-frugal (FFT) decision tree to evaluate the impact of VTE prevention on health outcomes and costs. We employed three methods: the "best evidence" from ASH guidelines, a "learning health system paradigm" combining guideline and real-world data from the Medical University of South Carolina (MUSC), and a "real-world data" approach based solely on MUSC data retrospectively extracted from electronic records. We found the most effective VTE prevention strategy utilizes the FFT decision tree, guided by an IMPROVE VTE score of ≥ 2 or ≥ 4 and a bleeding score of < 7. This method could prevent 45% of unnecessary treatments, saving about $5 million annually for patients like the MUSC cohort. We recommend integrating the IMPROVE models into hospital electronic medical records as a point-of-care tool, thereby enhancing VTE prevention in hospitalized medical patients.

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis.

BACKGROUND: Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. OBJECTIVE: The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. METHODS: We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. RESULTS: A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). CONCLUSIONS: This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.