Citations

Background: Artificial intelligence (AI), particularly generative and large language models, is being used in nursing education, practice, and scholarly writing. Generative AI applications have been specifically examined for their use in conducting literature reviews with evidence supporting reduced production time of scholarly work. However, there has been limited investigation of their levels of accuracy with identifying references for a literature review. Objective: The purpose of this study was to compare human-generated citations of literature reviews with AI literature-review generated citations. Methods: Using a comparative exploratory design, references from 4 human-written literature reviews, 2 published and 2 unpublished, on 4 different topics, were compared to references derived from 2 AI literature applications, Consensus and Elicit. Three prompting strategies were utilized, including prompts generated using ChatGPT-4. Agreement between the AI and human references was evaluated. Results: The percent of agreement between AI and human generated reference lists ranged from 0% to 63.6%. The Consensus application had a greater overall mean rate of match (21.3%) as compared to Elicit (3.7%). The use of a ChatGPT-4 prompt did not significantly impact results, and there were no differences based on published or unpublished literature reviews. Conclusion: The 2 literature-based applications examined in this study offered a glimpse of their potential use and limitations. The use of an AI literature review application may support but not replace human work.

Abstract Objectives The primary objective of this research is to assess the content coverage of nursing data within a publicly available common data model (CDM), focusing on how nursing data, documented in flowsheets, are represented within the model. Materials and Methods This mapping study was informed by previous evaluation studies and serves as a framework for evaluating information resources, including to guide development and implementation. The overall research process consists of 4 steps: (1) identify a CDM; (2) define evaluation criteria; (3) map nursing flowsheet data; and (4) apply evaluation criteria. Results Overall, 65.5% (n = 1170) of the flowsheet concepts were mapped to Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC) target codes and 56.0% (n = 1831) of the flowsheet values were mapped to SNOMED CT and LOINC target codes. The flowsheet concepts had a higher average mapping time per concept/reviewer (1.19 min) as compared to the average mapping time per value/reviewer (0.64 min). Discussion This mapping study demonstrated the progress and ongoing challenges of mapping nursing data to a national common data model. However, the ability to use nursing data at scale in a national CDM remains limited until more comprehensive mapping is completed. Conclusion This mapping study identifies a significant gap in integrating nursing data into a national common data model, highlighting an opportunity to enhance patient care through improved real-time insights and evidence-based nursing practices. Addressing this gap can help shape policies that prioritize the inclusion of nursing data. Additionally, aligning nursing data at scale can advance research, increase efficiency, and optimize nurse-sensitive patient outcomes.

The TransMet trial and Norwegian studies have shown excellent outcomes in highly selected patients undergoing liver transplant (LT) for unresectable colorectal liver metastases (uCRLM). Our aim is to provide an updated analysis of the current state of LT for uCRLM in the United States. Adult patients with a diagnosis of uCRLM listed for LT between December 2017 and March 2025 were identified from the United Network for Organ Sharing database and analyzed. An analysis of living donor LT (LDLT) and deceased donor LT (DDLT) was performed. During this period, 222 patients were listed, and 158 received an LT across 33 centers. Of these, 76 (48.1%) underwent LDLT, and 82 patients (51.9%) underwent DDLT. Of the 82 deceased donors, 56 (68.2%) were out-of-sequence offers. Overall, the 3-year survival and disease-free survival rates were 66.7% and 44.0%, respectively. The 3-year survival for LDLT and DDLT was 74.1% and 57.3% ( p =0.04), respectively. While these results have not yet reached the acceptable threshold established for other LT indications, they have improved and are expected to continue improving after the implementation of prioritization points. Given the potential variability of oncologic selection criteria and graft quality, we call for the standardization of nationally accepted selection criteria for listing patients for LT with uCRLM and for providing early access to high-quality organs. To achieve this, it is imperative to create, update, and maintain national databases that allow for the collection of more granular oncological variables.

Living donor liver transplantation for oncologic indications is a complex topic that involves transplantation, oncology, and ethical considerations. The incidence of primary and secondary malignancy of the liver is growing. As transplant oncology continues to provide promising outcomes, the patient population that may benefit from transplantation will grow. In this review, we provide evidence that justify attempting transplantation for oncologic indications through the exploration of outcomes in both deceased donor and living donor liver transplants. This will focus on outcomes for patients with hepatocellular carcinoma, cholangiocarcinoma, neuroendocrine liver metastasis, and colorectal liver metastases. This review aims to summarize the current status of transplant oncology and to develop the considerations of living donor liver transplantation for these indications.

Purpose of review Liver transplantation for metastatic colorectal cancer has been shown to be efficacious in the well selected patient. In the United States, there remains controversy on the appropriate selection criteria and optimal graft type to be utilized in these patients. Our group advocates for strict recipient selection and early access to quality grafts for these recipients. Recent findings In the past two years, there has been an explosion of centers reporting outcomes after liver transplantation for colorectal liver metastases. In North America, the publications have focused on single center experiences. The group in Oslo has reported their long-term outcomes of all transplanted patients. The TransMet randomized controlled trial has demonstrated efficacy of liver transplantation with chemotherapy over chemotherapy alone. Summary Liver transplantation for metastatic colorectal cancer is an efficacious procedure for the well selected patient. Regardless of graft type, potential liver transplant recipients with liver limited unresectable colorectal liver metastases should be evaluated with a strict criterion to determine eligibility. Once eligible, patients should receive early access to high quality grafts.

AbstractRegistered nurses increasingly work in remote care and digital interaction roles, offering flexibility and expansion of their scope of practice. These roles may expose nurses to digital compassion fatigue, a phenomenon proposed to be characterized by the negative psychological and emotional impact of caring for patients remotely through the use of technology.The first objective of this work is to propose the phenomenon of “digital compassion fatigue” as a potentially further evolved and differentiated form of “compassion fatigue.” The second objective is to produce a comparative analysis of attributes, antecedents, and consequences through literature reviews.An evolutionary concept analysis approach was selected as a guide for exploring, discussing, and defining the new concept of digital compassion fatigue. Concept analysis was selected given the need for a framework that accounts for the dynamic nature of technology and practice. The process of conducting a concept analysis includes consideration of diverse and multidisciplinary perspectives. As a result, those in caring, educational, and/or support service roles (e.g., social work, counseling, and teaching) for which distance suffering and technostress could feasibly be present were also included in the literature searches and reviews. Health care-specific studies often included nurses in the sample but may not have differentiated their specific insights or data points in the results.The concept analysis explored the attributes, antecedents, and consequences of digital compassion fatigue, differentiating it from its evolutionary parent, compassion fatigue. Key antecedents included technostress, distant suffering, and the unique challenges of delivering care remotely. A major confounding variable was the COVID-19 pandemic, which may have heightened or introduced new technology-related stressors or highlighted the existence of digital compassion fatigue.Further defining and understanding digital compassion fatigue is crucial for developing effective strategies to support nurses who may experience it or who are at risk.