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BackgroundAn operational definition of organ dysfunction applicable to neonates that predicts mortality in the setting of infection is lacking. We determined the utility of an objective, electronic health record (EHR)-automated, neonatal sequential organ failure assessment (nSOFA) score to predict mortality from late-onset sepsis (LOS) in premature, very low birth weight (VLBW) infants.MethodsRetrospective, single-center study of bacteremic preterm VLBW newborns admitted between 2012 and 2016. nSOFA scores were derived for patients with LOS at multiple time points surrounding the sepsis evaluation.ResultsnSOFA scores at evaluation and at all points measured after evaluation were different between survivors and non-survivors. Among patients with an nSOFA score of >4, mortality was higher at evaluation (13% vs 67%, p < 0.001), +6 h (15% vs 64%, p = 0.002), and +12 h (7% vs 71%, p < 0.001) as compared to patients with a score of ≤4. Receiver operating characteristics area under the curve was 0.77 at evaluation (95% CI 0.62–0.92; p = 0.001), 0.78 at +6 h (0.66–0.92; p < 0.001), and 0.93 at +12 h (0.86–0.997; p < 0.001).ConclusionsThe nSOFA scoring system predicted mortality in VLBW infants with LOS and this automated system was integrated into our EHR. Prediction of LOS mortality is a critical step toward improvements in neonatal sepsis outcomes.

Pregnant women are a vulnerable group who are needed in clinical research studies to advance prevention and treatment options for this population. Yet, pregnant women remain underrepresented in clinical research. Through the lens of the socioecological model, we highlight reported barriers and facilitators to recruitment and retention of pregnant women in studies that sought their participation. We trace historical, policy-based reasons for the exclusion of pregnant women in clinical studies to present-day rationale for inclusion of this group. The findings highlight why it has been difficult to recruit and retain this population over time. A body of literature suggests that integrative sampling and recruitment methods that leverage the influence and reach of prenatal providers will overcome recruitment challenges. We argue that these strategies, in combination with building strong engagement with existing community-based organizations, will enable teams to more effectively promote and retain pregnant women in future longitudinal cohort studies.

Examining stem cell biology from a philosophy of science perspective, this book clarifies the field's central concept, the stem cell, as well as its aims, methods, models, explanations and evidential challenges. The first chapters discuss what stem cells are, how experiments identify them, and why these two issues cannot be completely separated. The basic concepts, methods and structure of the field are set out, as well as key limitations and challenges. The second part of the book shows how rigorous explanations emerge from stem cell experiments, and compares these to other kinds of scientific explanation. Model organisms, the role of genes, and the significance of collaboration are also discussed. The last part of the book considers relations to systems biology and clinical medicine, arguing that both the mathematical models of the former, and ethical principles of the latter, are necessary for stem cell biology to deliver on its promises.

Large language models (LLMs) have flourished and gradually become an important research and application direction in the medical field. However, due to the high degree of specialization, complexity, and specificity of medicine, which results in extremely high accuracy requirements, controversy remains about whether LLMs can be used in the medical field. More studies have evaluated the performance of various types of LLMs in medicine, but the conclusions are inconsistent. This study uses a network meta-analysis (NMA) to assess the accuracy of LLMs when answering clinical research questions to provide high-level evidence-based evidence for its future development and application in the medical field. In this systematic review and NMA, we searched PubMed, Embase, Web of Science, and Scopus from inception until October 14, 2024. Studies on the accuracy of LLMs when answering clinical research questions were included and screened by reading published reports. The systematic review and NMA were conducted to compare the accuracy of different LLMs when answering clinical research questions, including objective questions, open-ended questions, top 1 diagnosis, top 3 diagnosis, top 5 diagnosis, and triage and classification. The NMA was performed using Bayesian frequency theory methods. Indirect intercomparisons between programs were performed using a grading scale. A larger surface under the cumulative ranking curve (SUCRA) value indicates a higher ranking of the corresponding LLM accuracy. The systematic review and NMA examined 168 articles encompassing 35,896 questions and 3063 clinical cases. Of the 168 studies, 40 (23.8%) were considered to have a low risk of bias, 128 (76.2%) had a moderate risk, and none were rated as having a high risk. ChatGPT-4o (SUCRA=0.9207) demonstrated strong performance in terms of accuracy for objective questions, followed by Aeyeconsult (SUCRA=0.9187) and ChatGPT-4 (SUCRA=0.8087). ChatGPT-4 (SUCRA=0.8708) excelled at answering open-ended questions. In terms of accuracy for top 1 diagnosis and top 3 diagnosis of clinical cases, human experts (SUCRA=0.9001 and SUCRA=0.7126, respectively) ranked the highest, while Claude 3 Opus (SUCRA=0.9672) performed well at the top 5 diagnosis. Gemini (SUCRA=0.9649) had the highest rated SUCRA value for accuracy in the area of triage and classification. Our study indicates that ChatGPT-4o has an advantage when answering objective questions. For open-ended questions, ChatGPT-4 may be more credible. Humans are more accurate at the top 1 diagnosis and top 3 diagnosis. Claude 3 Opus performs better at the top 5 diagnosis, while for triage and classification, Gemini is more advantageous. This analysis offers valuable insights for clinicians and medical practitioners, empowering them to effectively leverage LLMs for improved decision-making in learning, diagnosis, and management of various clinical scenarios. PROSPERO CRD42024558245; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024558245.

This book traces the genesis, principles and practice of homeopathy, and discusses the reasons for its enduring popularity. Two hundred years ago, medicine had little to offer except blood letting and the administration of violent purgatives--practices which shortened the course of illness by hastening the death of the patient. Largely in reaction to what he correctly saw as the brutality and ineffectiveness of the medicine of his day, the eighteenth century German physician Samuel Hahnemann developed a system of therapeutics that he termed homeopathy. Ironically, while modern medicine has changed beyond recognition, homeopathy, with its roots in alchemy and metaphysics, continues to be practiced precisely as it was in Hahnemann's day. Readers of this book will enjoy the story of homeopathy and its almost magical attraction, whilst learning much from the authors' rational and scientific discussion of the biological, chemical and psychological questions that this treatment raises.

BACKGROUNDThe effects of antibiotics on infant gut microbiota are unclear. We hypothesized that the use of common antibiotics results in long-term aberration in gut microbiota.METHODSAntibiotic-naive infants were prospectively recruited when hospitalized because of a respiratory syncytial virus infection. Composition of fecal microbiota was compared between those receiving antibiotics during follow-up (prescribed at clinicians’ discretion because of complications such as otitis media) and those with no antibiotic exposure. Fecal sampling started on day 1, then continued at 2-day intervals during the hospital stay, and at 1, 3 and 6 months at home.RESULTSOne hundred and sixty-three fecal samples from 40 patients (median age 2.3 months at baseline; 22 exposed to antibiotics) were available for microbiota analyses. A single course of amoxicillin or macrolide resulted in aberration of infant microbiota characterized by variation in the abundance of bifidobacteria, enterobacteria and clostridia, lasting for several months. Recovery from the antibiotics was associated with an increase in clostridia. Occasionally, antibiotic use resulted in microbiota profiles associated with inflammatory conditions.CONCLUSIONSAntibiotic use in infants modifies especially bifidobacterial levels. Further studies are warranted whether administration of bifidobacteria will provide health benefits by normalizing the microbiota in infants receiving antibiotics.

\"The standard reference in the field, this acclaimed work synthesizes findings from hundreds of carefully selected studies of mental health treatments for children and adolescents. Chapters on frequently encountered clinical problems systematically review the available data, identify gaps in what is known, and spell out recommendations for evidence-based practice. The authors draw on extensive clinical experience as well as research expertise. Showcasing the most effective psychosocial and pharmacological interventions for young patients, they also address challenges in translating research into real-world clinical practice. New to This Edition *Incorporates over a decade of research advances and evolving models of evidence-based care. *New chapter topic: child maltreatment. *Separate chapters on self-injurious behavior, eating disorders, and substance use disorders (previously covered in a single chapter on self-harming disorders). *Expanded chapters on depression, anxiety, and conduct disorder. *Includes reviews of the expanding range of manualized psychosocial \"treatment packages\" for children.\"-- Provided by publisher.

Background This study aimed to investigate the prevalence of and the factors associated with post-COVID-2019 condition in COVID-19 children and adolescents in Germany. Methods The present retrospective cohort study used data from the Disease Analyzer database (IQVIA), and included patients aged <18 years who were diagnosed with COVID-19 in one of 524 general and 81 pediatric practices in Germany between October 2020 and August 2021 (index date: first COVID-19 diagnosis). Post-COVID-19 condition was assessed between the index date and November 2021. Covariates included age, sex, type of practice, and chronic conditions documented in at least 1% of the population. Results There were 6568 children and adolescents included in this study (mean [SD] age 10.1 [4.9] years; 49.2% girls). The prevalence of post-COVID-19 condition was 1.7% in the population. Patients aged 13–17 years were more likely to be diagnosed with post-COVID-19 condition compared with those being aged ≤5 years (RR = 3.14). Anxiety disorders (RR = 2.53), somatoform disorders (RR = 2.11), and allergic rhinitis (RR = 2.02) were also significantly associated with post-COVID-19 condition. Conclusion Post-COVID-19 condition was rare in COVID-19 children and adolescents in Germany. Data from other settings are warranted to confirm these findings. Impact The prevalence of post-COVID-19 condition was 1.7% in this population of children and adolescents. Older children and adolescents were more likely to be diagnosed with post-COVID-19 condition than their younger counterparts. Anxiety disorders, somatoform disorders, and allergic rhinitis were significantly associated with post-COVID-19 condition. More data from other settings and countries are warranted to corroborate or refute these findings.