Introduction: Since the publication of the first European Society for the Study of Coeliac Disease (ESsCD) guidelines in 2019, significant advancements have emerged in the diagnosis of coeliac disease (CeD) in adults. These 2025 guidelines incorporate new evidence to refine diagnostic strategies, aiming for improved accuracy of testing, and enhance overall quality of clinical care. Methods: A multidisciplinary panel of experts revised the ESsCD guidelines using the AGREE II instrument (Appraisal of Guidelines for Research and Evaluation II) and the GRADE methodology (The Grading of Recommendations Assessment, Development, and Evaluation). Clinical questions were structured using the PICO format, and statements and recommendations were finalised through a Delphi consensus process. Literature quality was assessed using AMSTAR‐2 and QUADAS‐2 tools. Results: The updated guidelines are presented in two parts. Part 1 focuses on adult CeD diagnosis, introducing major changes such as a conditional no‐biopsy approach for selected adults with high‐titre IgA anti‐TG2 serology (≥ 10 × ULN). Regarding serology, the use of validated high‐performance ELISAs displaying a high diagnostic accuracy is emphasised, while routine use of IgA anti‐Endomysium serology is no longer recommended for confirmation. Revised duodenal biopsy protocols now mandate at least four samples from the second part of the duodenum, with bulb biopsies conditionally included. The guidelines provide structured approaches for diagnosing potential CeD, seronegative villous atrophy, and CeD in individuals already on a gluten‐free diet. HLA‐DQ2/DQ8 typing is recommended for diagnostic clarification in select cases. Conclusions: The updated 2025 ESsCD guidelines provide a comprehensive framework for the diagnosis of CeD in adults. By integrating evolving diagnostic strategies, minimising over‐testing, and patient‐centred care approaches, they aim to optimise patient outcomes, quality of life and use of diagnostic resources at the same time.

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices.

Researchers commonly use automated solutions such as Natural Language Processing (NLP) systems to extract clinical information from large volumes of unstructured data. However, clinical text's poor semantic structure and domain-specific vocabulary can make it challenging to develop a one-size-fits-all solution. Large Language Models (LLMs), such as OpenAI's Generative Pre-Trained Transformer 3 (GPT-3), offer a promising solution for capturing and standardizing unstructured clinical information. This study evaluated the performance of InstructGPT, a family of models derived from LLM GPT-3, to extract relevant patient information from medical case reports and discussed the advantages and disadvantages of LLMs versus dedicated NLP methods. In this paper, 208 articles related to case reports of foreign body injuries in children were identified by searching PubMed, Scopus, and Web of Science. A reviewer manually extracted information on sex, age, the object that caused the injury, and the injured body part for each patient to build a gold standard to compare the performance of InstructGPT. InstructGPT achieved high accuracy in classifying the sex, age, object and body part involved in the injury, with 94%, 82%, 94% and 89%, respectively. When excluding articles for which InstructGPT could not retrieve any information, the accuracy for determining the child's sex and age improved to 97%, and the accuracy for identifying the injured body part improved to 93%. InstructGPT was also able to extract information from non-English language articles. The study highlights that LLMs have the potential to eliminate the necessity for task-specific training (zero-shot extraction), allowing the retrieval of clinical information from unstructured natural language text, particularly from published scientific literature like case reports, by directly utilizing the PDF file of the article without any pre-processing and without requiring any technical expertise in NLP or Machine Learning. The diverse nature of the corpus, which includes articles written in languages other than English, some of which contain a wide range of clinical details while others lack information, adds to the strength of the study.

Northern Italy has been the first European area affected by the COVID-19 pandemic and related social restrictive measures. We sought to evaluate the impact of the COVID-19 outbreak on PICU admissions in Northern Italy, using data from the Italian Network of Pediatric Intensive Care Units Registry. We included all patients admitted to 4 PICUs from 8-weeks-before to 8-weeks-after February 24^th, 2020, and those admitted in the same period in 2019. Incidence rate ratios (IRR) evaluating incidence rate differences between pre- and post-COVID-19 periods in 2020 (IRR-1), as well as between the post-COVID-19-period with the same period in 2019 (IRR-2), were computed using zero-inflated negative binomial or Poisson regression modeling. A total of 1001 admissions were included. The number of PICU admissions significantly decreased during the COVID-19 outbreak compared to pre-COVID-19 and compared to the same period in 2020 (IRR-1 0.63 [95%CI 0.50–0.79]; IRR-2 0.70 [CI 0.57–0.91]). Unplanned and medical admissions significantly decreased (IRR-1 0.60 [CI 0.46–0.70]; IRR-2 0.67 [CI 0.51–0.89]; and IRR-1 0.52, [CI 0.40–0.67]; IRR-2 0.77 [CI 0.58–1.00], respectively). Intra-hospital, planned (potentially delayed by at least 12 h), and surgical admissions did not significantly change. Patients admitted for respiratory failure significantly decreased (IRR-1 0.55 [CI 0.37–0.77]; IRR-2 0.48 [CI 0.33–0.69]). Conclusions: Unplanned and medical PICU admissions significantly decreased during COVID-19 outbreak, especially those for respiratory failure. What is Known: • Northern Italy has been the first European area affected by the COVID-19 pandemic. • Although children are relatively spared from the severe COVID-19 disease, the pediatric care system has been affected by social restrictive measures, with a reported 73–88% reduction in pediatric emergency department admissions. What is New: • Unplanned and medical PICU admissions significantly decreased during the COVID-19 outbreak compared to pre-COVID-19 and to the same period in 2019, especially those for respiratory failure. Further studies are needed to identify associated factors and new prevention strategies.