Anaesthesia contributes to greenhouse gas emissions and can play a crucial role in reducing the carbon footprint of the global healthcare sector. The aim of this systematic review is to identify which departmental interventions influence estimated carbon dioxide (CO₂) equivalent emissions of anaesthesia and to quantify their reductions. A systematic literature search was conducted through four major electronic databases (Cochrane Library, Embase, MEDLINE, and PubMed) for studies investigating the changes in CO₂ equivalent emissions per anaesthetic before and after departmental green anaesthesia interventions. Data were extracted by two independent reviewers. The primary outcomes were mean decrease of CO₂ equivalents in kilograms per anaesthetic and total decrease in CO₂ equivalents in tonnes. The mean effect was calculated as percentage change per patient and in total. Of 3987 screened studies, 13 met the criteria for quantitative synthesis and showed low to moderate risk of bias. The following types of departmental sustainability interventions were found: education of staff, decreased use of desflurane and sevoflurane, promotion of TIVA, use of low fresh gas flows, proper waste management, and formation of green teams. The postinterventional total mean decrease of CO₂ equivalents in kilograms per anaesthetic was 68.2% (18.6%) and in total tonnes was 75.2% (16.3%). Our analysis demonstrates the substantial CO₂ reduction potential inherent in sustainable anaesthesia programmes. Currently available literature supports staff education on avoidance of desflurane, reduction of volatile anaesthesia, lower fresh gas flow, increased utilisation of TIVA, and implementation of proper waste management protocols in operating rooms as potentially effective interventions.

Background: Post-intensive care syndrome encompasses physical, cognitive, and psychological impairments that persist in patients after discharge from an intensive care unit. There is considerable variation in the tools used for assessment. This systematic review aimed to summarize the consensus-based recommendations for assessing post-intensive care syndrome. Methods: A comprehensive literature search identified four consensus-based guidelines. A quality assessment carried out using the Appraisal of Guidelines for Research and Evaluation II tool demonstrated high methodological standards across all the included papers. Results: The guidelines consistently emphasize assessing cognition, mental health, and physical function as the core domains. However, there are notable differences in the specific tools recommended. Major et al. focused on physical examinations, while Mikkelsen et al. proposed a fundamental package of five tools covering the key domains. Spies et al. aimed for a pragmatic set of freely available instruments administrable within 30 min. Nakanishi et al. provided a detailed ranking of instruments for each domain. The availability of validated translations varied considerably across languages. Some tools developed specifically for post-intensive care syndrome were not considered by any consensus conference. Conclusions: Further work is needed to establish a universally accepted standard for assessing post-intensive care syndrome that considers practical implementation across diverse settings and languages.

Introduction Chronic pain is a public health issue, leading to substantial healthcare costs and diminished quality of life for sufferers. While the role of anxiety in pain modulation has been extensively studied, the effects of other emotional states on the body’s pain control mechanisms remain less understood. This study sought to explore how different emotions (happiness, anger, sadness, and interest) affect conditioned pain modulation (CPM) and the wind-up phenomenon in healthy adults. Methods This randomized controlled, cross-over trial involved 28 healthy participants aged 18–60. Participants watched video clips designed to induce specific emotions: happiness, anger, sadness, and interest. Emotional states were assessed using a 7-point Likert scale. Pain modulation was measured using CPM and the wind-up phenomenon. CPM was assessed with a hot water bath as the conditioning stimulus and pressure pain tolerance as the test stimulus. Wind-up was measured using pinprick needle stimulators and a visual analog scale. Data were analyzed using paired t tests to compare pre- and post-emotion induction values. Results Significant changes in emotional self-assessment values were observed for all emotions. Happiness increased CPM (4.6 ± 11.4, p = 0.04277), while sadness − 9.9 ± 23.1, p = 0.03211) and anger − 9.1 ± 23.3, p = 0.04804) decreased it. Interest did not significantly alter CPM (− 5.1 ± 25.8, p = 0.31042). No significant effects were found for the wind-up phenomenon across any emotional states. Conclusion This study shows that emotional states significantly affect the body’s ability to modulate pain. Positive emotions like happiness enhance pain inhibition, while negative emotions such as sadness and anger impair it. These findings suggest that emotional modulation techniques could be integrated into pain management strategies to improve patient outcomes. Further research should explore a broader range of emotions and include objective measures to validate these results.

The implementation of low-carbon healthcare practices will be significantly enhanced by the role of anesthesia personnel. While there is a lack of data on the specific measures being implemented by anesthesia departments in Austria, we conducted interviews with six experts in sustainability within anesthesia to address this knowledge gap. These experts provided insights on strategies for reducing the CO₂ impact in the operating theatre, the level of interest among anesthetists in sustainability, the role of green teams in hospitals, and future prospects for sustainable anesthesia. While Austria has made progress in reducing the use of Desfluran, waste separation within operating theatres remains a significant issue. Green teams are present in hospitals, but there is a need for the greater inclusion of anesthetists and clinical staff. The topic of sustainability is becoming increasingly important in the field of anesthesia, and the past three years have witnessed a significant push towards reducing CO₂ emissions in hospitals across Austria. The experts identified key steps towards achieving sustainable anesthesia, emphasizing the need for an internal motivation to drive meaningful change. This study highlights the numerous measures that have already been implemented in the pursuit of sustainability in anesthesia and the ongoing efforts towards further improvement.

Background: Generative artificial intelligence (GenAI) tools are increasingly used in scientific research to support literature searches, evidence synthesis, and manuscript preparation. While these systems promise substantial efficiency gains, concerns have emerged regarding their reliability, particularly their tendency to cite inaccurate, fabricated, or retracted literature. The unrecognized inclusion of retracted studies poses a serious risk to research integrity and evidence-based decision-making. Whether commonly used GenAI tools can reliably detect, exclude, or transparently communicate the retraction status of scientific publications remains unclear. Objective: This study aimed to evaluate the ability of freely available GenAI tools to correctly handle retracted scientific articles during literature searches. Primary and secondary outcomes focused on accuracy, reliability, and consistency in recognizing retracted literature. Methods: In this pragmatic trial, nine widely used free-access GenAI tools (ChatGPT 4, ChatGPT 5, Claude, Gemini, Perplexity, Microsoft Copilot, SciSpace, ScienceOS, and Consensus) were evaluated. Each tool was asked five predefined, standardized questions addressing topic overview, article identification, article summarization, and explicit assessment of retraction status. Overall, 15 retracted articles (the 10 most cited and 5 most recently retracted as of May 23, 2025) were selected from the Retraction Watch database. All questions were repeated twice to assess intratool consistency. Responses were independently rated as correct or incorrect by 2 researchers. Descriptive statistics summarized performance, and comparisons between general-purpose and research-focused AI tools were conducted using descriptive statistics. Interreviewer agreement was assessed using Cohen kappa coefficient. Results: None of the evaluated AI tools consistently handled retracted articles correctly. No model achieved perfect accuracy across all question sets. ChatGPT 5 performed best, defined by the primary outcome of achieving fully correct responses to all five predefined tasks (5/5) for the highest number of retracted articles, correctly answering all five questions for 8 of 15 articles (53.3%). Research-focused tools (SciSpace, ScienceOS, and Consensus) failed to produce a single fully correct response set. Retracted articles were frequently included in topic overviews without warning, with error rates exceeding 40% in several tools. When specifically asked about retraction status, most systems failed to provide correct or complete information. OpenEvidence only reported data for a subset of our retracted articles as it is only used in health care literature. It demonstrated strong performance in topic overviews but low accuracy in identifying retracted articles. Conclusions: Freely available GenAI tools are currently not able to detect, exclude, or appropriately flag retracted scientific literature. The widespread and confident reproduction of retracted studies represents a substantial threat to research integrity, particularly in medical and evidence-based fields. Until retraction-aware verification mechanisms are systematically integrated, independent source checking remains essential when using AI-assisted literature tools.