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[...]one of the trials included in the summary, Belohlavek et al. [...]we will improve our ability to integrate real-time information and reduce the rate of “hallucinations”, just as the nascent field of prompt engineering evolves. Effect of intra-arrest transport, extracorporeal cardiopulmonary resuscitation, and immediate invasive assessment and treatment on functional neurologic outcome in refractory out-of-hospital cardiac arrest: a randomized clinical trial.

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community’s understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13 th , 2023, to September 1 st , 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.

Background The goal of the study was to evaluate a potential role for tumor necrosis factor alpha (TNF-α) genetic variability as biomarker in sepsis. In particular, we aimed to determine if single nucleotide polymorphisms (SNPs) of TNF-α gene are associated with sepsis in terms of risk, severity and outcome. Methods We performed a prospective study on 163 adult critically ill septic patients (septic shock 65, sepsis 98, further divided in 40 survivors and 123 deceased) and 232 healthy controls. Genotyping of TNF-α SNPs (-308G/A, -238G/A, -376G/A and +489G/A) was performed for all patients and controls and plasma cytokine levels were measured during the first 24 h after sepsis onset. Results TNF-α  +489G/A A-allele carriage was associated with significantly lower risk of developing sepsis and sepsis shock (AA+AG vs GG: OR = 0.53; p  = 0.004; 95% CI = 0.34–0.82 and OR = 0.39; p  = 0.003; 95% CI = 0.21–0.74, respectively) but not with sepsis-related outcomes. There was no significant association between any of the other TNF-α promoter SNPs, or their haplotype frequencies and sepsis or septic shock risk. Circulating TNF-α levels were higher in septic shock; they were not correlated with SNP genotype distribution; GG homozygosity for each polymorphism was correlated with higher TNF-α levels in septic shock. Conclusions TNF-α  +489G/A SNP A-allele carriage may confer protection against sepsis and septic shock development but apparently does not influence sepsis-related mortality. Promoter TNF-α SNPs did not affect transcription and were not associated with distinct sepsis, septic shock risk or outcomes.

Background Over 50% of all critically ill children develop preventable intensive care unit-acquired morbidity. Early and progressive mobility is associated with improved outcomes in critically ill adults including shortened duration of mechanical ventilation and improved muscle strength. However, the clinical effectiveness of early and progressive mobility in the pediatric intensive care unit has never been rigorously studied. The objective of the study is to evaluate if the PICU Up! intervention, delivered in real-world conditions, decreases mechanical ventilation duration (primary outcome) and improves delirium and functional status compared to usual care in critically ill children. Additionally, the study aims to identify factors associated with reliable PICU Up! delivery. Methods The PICU Up! trial is a stepped-wedge, cluster-randomized trial of a pragmatic, interprofessional, and multifaceted early mobility intervention (PICU Up!) conducted in 10 pediatric intensive care units (PICUs). The trial’s primary outcome is days alive free of mechanical ventilation (through day 21). Secondary outcomes include days alive and delirium- and coma-free (ADCF), days alive and coma-free (ACF), days alive, as well as functional status at the earlier of PICU discharge or day 21. Over a 2-year period, data will be collected on 1,440 PICU patients. The study includes an embedded process evaluation to identify factors associated with reliable PICU Up! delivery. Discussion This study will examine whether a multifaceted strategy to optimize early mobility affects the duration of mechanical ventilation, delirium incidence, and functional outcomes in critically ill children. This study will provide new and important evidence on ways to optimize short and long-term outcomes for pediatric patients. Trial registration ClinicalTrials.gov NCT04989790. Registered on August 4, 2021.

On our way to AI-driven care algorithms and bedside genomics, we should be mindful of the care our patients are not receiving. [...]we improve at our core task – delivering proven therapies consistently and effectively at the bedside – the promise of personalized medicine will remain just that: a promise. Xu J, Shi W, Xie L, Xu J, Bian L. Feeding Intolerance in Critically Ill Patients with Enteral Nutrition: A Meta-Analysis and Systematic Review. Sharma SK, Rani R, Thakur K. Effect of Early Versus Delayed Parenteral Nutrition on the Health Outcomes of Critically Ill Adults: A Systematic Review.

[...]LLMs could swiftly synthesize and summarize copious amounts of medical literature, assimilate data from various patient chart sources, and apply the latest guidelines to support decision-making—a computer boasting perfect memory that seemingly “comprehends” clinical context and pathology. Association of hydroxyethyl starch administration with mortality and acute kidney injury in critically ill patients requiring volume resuscitation: a systematic review and meta-analysis. Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department. Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US.

The coronavirus was identified in a wet food market in Wuhan, China, and has been the subject of a robust public health response by both Chinese authorities and the international community ever since. Despite the differences between the SARS, MERS and novel coronavirus, the similarities within the beta-CoV genus allow us to extrapolate from our previous experience with corona-virus outbreaks and increase our understanding of the current one. World Health Organization; 2020. htps://www.who.int/publications-detail/clinical-management-of-severe-acute-respiratory-infection-when-novel-coronavirus-(ncov)-infection-is-suspected 6 Huang C, Wang Y, Li X, et al.

For the SARS-CoV-2 pandemic to have taken on its current dimensions, two conditions must have been met: first, a viral transmission pattern that was unusually infectious, and second, an ineffective public health response. [...]mixed public health messaging undermined public confidence and led to skepticism in adopting social distancing measures when they were implemented. The asymptomatic spread combined with an ineffective public health response made our January 2020 prediction—that SARS-CoV-2 could be easily contained by identifying and quarantining symptomatic patients—nearly impossible to come to fruition. [...]pediatric patients originally thought to be at low risk of developing severe complications with SARS-CoV-2 infection have required ICU care—most commonly for a COVID-related Multisystem Inflammatory Syndrome in Children (MIS-C).

NOABSTRACTIllness severity scoring tools, such as PRISM III/IV, PIM-3, and PELOD-2, are widely used in pediatric critical care research. However, their application is hindered by complex calculation processes, privacy concerns with third-party online calculators, and challenges in accurate implementation within statistical packages.We have developed a comprehensive, open-source toolkit for implementing the PIM-3, Simplified PIM-3, and PELOD-2 scores. The toolkit includes the pim3 and pelod2 commands and is compatible with Stata versions 12 and above. It features robust data validation, error messaging, a graphical interface, and support for SI and Imperial units. The toolkit's accuracy was validated through unit testing and synthetic data, comparing results with existing implementations.In performance tests, the toolkit exhibited a median processing time of 21.82 seconds for PELOD-2, 14.06 seconds for PIM-3, and 9.74 seconds for Simplified PIM-3, when applied to datasets of 10,000,000 records. It consistently achieved 100% accuracy in both synthetic data tests and manual spot checks.The toolkit decreases processing time and improves accuracy in calculating pediatric critical care severity scores such as PELOD-2, PIM-3, and Simplified PIM-3. Its application in large datasets and validation highlights its utility as a tool for streamlining pediatric critical care research.

The goal of the study was to evaluate a potential role for tumor necrosis factor alpha (TNF-[alpha]) genetic variability as biomarker in sepsis. In particular, we aimed to determine if single nucleotide polymorphisms (SNPs) of TNF-[alpha] gene are associated with sepsis in terms of risk, severity and outcome. We performed a prospective study on 163 adult critically ill septic patients (septic shock 65, sepsis 98, further divided in 40 survivors and 123 deceased) and 232 healthy controls. Genotyping of TNF-[alpha] SNPs (-308G/A, -238G/A, -376G/A and +489G/A) was performed for all patients and controls and plasma cytokine levels were measured during the first 24 h after sepsis onset. TNF-[alpha] +489G/A A-allele carriage was associated with significantly lower risk of developing sepsis and sepsis shock (AA+AG vs GG: OR = 0.53; p = 0.004; 95% CI = 0.34-0.82 and OR = 0.39; p = 0.003; 95% CI = 0.21-0.74, respectively) but not with sepsis-related outcomes. There was no significant association between any of the other TNF-[alpha] promoter SNPs, or their haplotype frequencies and sepsis or septic shock risk. Circulating TNF-[alpha] levels were higher in septic shock; they were not correlated with SNP genotype distribution; GG homozygosity for each polymorphism was correlated with higher TNF-[alpha] levels in septic shock. TNF-[alpha] +489G/A SNP A-allele carriage may confer protection against sepsis and septic shock development but apparently does not influence sepsis-related mortality. Promoter TNF-[alpha] SNPs did not affect transcription and were not associated with distinct sepsis, septic shock risk or outcomes.