Patients subject to major surgery, suffering sepsis, major trauma, or following cardiopulmonary bypass exhibit a systemic inflammatory response. This inflammatory response involves a complex array of inflammatory polypeptide molecules known as cytokines. It is well accepted that the loss of local control of the release of these cytokines leads to systemic inflammation and potentially deleterious consequences including the Systemic Inflammatory Response Syndrome, Multi-Organ Dysfunction Syndrome, shock and death. The Medline database was searched for literature on mechanisms involved in the development of SIRS and potential targets for modifying the inflammatory response. We focus on the novel therapy of cytokine adsorption as a promising removal technology. Accumulating data from human studies and experimental animal models suggests that both pro- and anti- inflammatory cytokines are released following a variety of initiating stimuli including endotoxin release, complement activation, ischaemia reperfusion injury and others. Pro-and anti-inflammatory cytokines interact in a complex and unpredictable manner to influence the immune system and eventually cause multiple end organ effects. Cytokine adsorption therapy provides a potential solution to improving outcomes following Systemic Inflammatory Response Syndrome.

Communities of practice (CoPs) are defined as "groups of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise by interacting on an ongoing basis". They are an effective form of knowledge management that have been successfully used in the business sector and increasingly so in healthcare. In May 2023 the electronic databases MEDLINE and EMBASE were systematically searched for primary research studies on CoPs published between 1st January 1950 and 31st December 2022. PRISMA guidelines were followed. The following search terms were used: community/communities of practice AND (healthcare OR medicine OR patient/s). The database search picked up 2009 studies for screening. Of these, 50 papers met the inclusion criteria. The most common aim of CoPs was to directly improve a clinical outcome, with 19 studies aiming to achieve this. In terms of outcomes, qualitative outcomes were the most common measure used in 21 studies. Only 11 of the studies with a quantitative element had the appropriate statistical methodology to report significance. Of the 9 studies that showed a statistically significant effect, 5 showed improvements in hospital-based provision of services such as discharge planning or rehabilitation services. 2 of the studies showed improvements in primary-care, such as management of hepatitis C, and 2 studies showed improvements in direct clinical outcomes, such as central line infections. CoPs in healthcare are aimed at improving clinical outcomes and have been shown to be effective. There is still progress to be made and a need for further studies with more rigorous methodologies, such as RCTs, to provide further support of the causality of CoPs on outcomes.

Background: Web-based content is rapidly becoming the primary source of health care information. There is a pressing need for web-based health care content to not only be accurate but also be engaging. Improved engagement of people with web-based health care content has the potential to inform as well as influence behavioral change to enable people to make better health care choices. The factors associated with better engagement with web-based health care content have previously not been considered. Objective: The aims of this study are to identify the factors that affect engagement with web-based health care content and develop a framework to be considered when creating such content. Methods: A comprehensive search of the PubMed and MEDLINE database was performed from January 1, 1946, to January 5, 2020. The reference lists of all included studies were also searched. The Medical Subject Headings database was used to derive the following keywords: “patient information,” “online,” “internet,” “web,” and “content.” All studies in English pertaining to the factors affecting engagement in web-based health care patient information were included. No restrictions were set on the study type. Analysis of the themes arising from the results was performed using inductive content analysis. Results: The search yielded 814 articles, of which 56 (6.9%) met our inclusion criteria. The studies ranged from observational and noncontrolled studies to quasi-experimental studies. Overall, there was significant heterogeneity in the types of interventions and outcome assessments, which made quantitative assessment difficult. Consensus among all authors of this study resulted in six categories that formed the basis of a framework to assess the factors affecting engagement in web-based health care content: easy to understand, support, adaptability, accessibility, visuals and content, and credibility and completeness. Conclusions: There is a paucity of high-quality data relating to the factors that improve the quality of engagement with web-based health care content. Our framework summarizes the reported studies, which may be useful to health care content creators. An evaluation of the utility of web-based content to engage users is of significant importance and may be accessible through tools such as the Net Promoter score. Web 3.0 technology and development of the field of psychographics for health care offer further potential for development. Future work may also involve improvement of the framework through a co-design process.

Web-based health care content has emerged as a primary source for patients to access health information without direct guidance from health care providers. The benefit of this approach is dependent on the ability of patients to access engaging high-quality information, but significant variability in the quality of web-based information often forces patients to navigate large quantities of inaccurate, incomplete, irrelevant, or inaccessible content. Personalization positions the patient at the center of health care models by considering their needs, preferences, goals, and values. However, the traditional methods used thus far in health care to determine the factors of high-quality content for a particular user are insufficient. Machine learning (ML) uses algorithms to process and uncover patterns within large volumes of data to develop predictive models that automatically improve over time. The health care sector has lagged behind other industries in implementing ML to analyze user and content features, which can automate personalized content recommendations on a mass scale. With the advent of big data in health care, which builds comprehensive patient profiles drawn from several disparate sources, ML can be used to integrate structured and unstructured data from users and content to deliver content that is predicted to be effective and engaging for patients. This enables patients to engage in their health and support education, self-management, and positive behavior change as well as to enhance clinical outcomes.

Appendicectomy has traditionally been a training operation for junior surgical trainees. With the increased incidence of laparoscopic appendicectomy, concern has increased about the safety of this far more technically demanding procedure in the hands of junior surgical trainees. The learning curve of a junior surgeon is presented. Consecutive patients having laparoscopic appendicectomy were studied. A 3-port Hasson technique was used. Patient demographics, conversion rate and reason for conversion, operation times, number of complicated cases (retrocecal position, dense adhesions, perforated/gangrenous/abscess associated appendicitis), and postoperative complications were recorded. The moving average and cumulative sum (CUSUM) methods were used to delineate the learning curve. Forty patients were studied. Median age was 24 (IQR: 18, 40). Twenty-nine (72.5%) patients were female. Data were not available for 3 patients (6%); the remaining patients form the basis of this study. A statistically significant improvement occurred in operating time between group 2 and group 3, P<0.0001 (95% CI, 21.23 to 47.99). The CUSUM plot demonstrates that the learning curve was surmounted by 20 cases performed. Laparoscopic appendicectomy is a safe procedure for junior trainees, and the learning curve stabilizes by 20 cases performed.