Programmed Intermittent Bolus versus Continuous Infusion for Regional Analgesia Following Thoracoscopic Surgery: A Systematic Review and Meta-Analysis.

Background: Continuous regional analgesia techniques have emerged as a more effective alternative for postoperative analgesia, but the clinical efficacy of infusion modes in thoracoscopic surgery remains controversial. This systematic review and meta-analysis to compare the efficacy of programmed intermittent bolus infusion (PIBI) with continuous infusion (CI) for regional analgesia in patients undergoing thoracoscopic surgery.

Methods: We searched PubMed, Embase, Web of Science, and the Cochrane Library for relevant research from inception to March 2025.

AND-1 is a critical regulator of R-loop dynamics and a target to overcome endocrine resistance.

R-loops are three-stranded DNA/RNA hybrids that are essential for various cellular pathways. However, when dysregulated, they lead to genomic instability and numerous human diseases. R-loops are tightly regulated, with RNase H1 acting as a key enzyme responsible for resolving DNA/RNA hybrids.

Toward Digital Twins in the Intensive Care Unit: A Medication Management Case Study.

Objective: To evaluate the efficacy of digital twins developed using a large language model (LLaMA-3), fine-tuned with Low-Rank Adapters (LoRA) on ICU physician notes, and to determine whether specialty-specific training enhances treatment recommendation accuracy compared to other ICU specialties or zero-shot baselines.

Materials And Methods: Digital twins were created using LLaMA-3 fine-tuned on discharge summaries from the MIMIC-III dataset, where medications were masked to construct training and testing datasets. The medical ICU dataset (1,000 notes) was used for evaluation, and performance was assessed using BERTScore and ROUGE-L.

Explaining alerts from a pediatric risk prediction model using clinical text.

Objective: Risk prediction models are used in hospitals to identify pediatric patients at risk of clinical deterioration, enabling timely interventions and rescue. The objective of this study was to develop a new explainer algorithm that uses a patient's clinical notes to generate text-based explanations for risk prediction alerts.

Materials And Methods: We conducted a retrospective study of 39 406 patient admissions to the American Family Children's Hospital at the University of Wisconsin-Madison (2009-2020).

Deep learning for automated grading of radiographic sacroiliitis.

Background: Grading assessment of sacroiliitis via X-ray is widely used in clinical evaluation. The aim of this study was to develop and validate an artificial intelligence (AI) system to help physicians in assessing and diagnosing sacroiliitis from standard X-ray images.

Methods: In this retrospective study, a deep learning model for the automated grading assessment of radiographic sacroiliitis was developed using pelvic X-ray images from a training set of 465 individuals (930 single sacroiliac joints) and a validation set of 195 individuals (390 single sacroiliac joints).

Qualitative and Quantitative Assessment of T2-Weighted Imaging Signal Variations in Prostate MRI Following Different Abstinence Periods: A Study in Young Healthy Men.

Objective: This study evaluates the impact of an abstinence period on the image quality of high-field prostate magnetic resonance imaging (MRI).

Methods: Male patients who underwent prostate MRI at Xi'an No.3 hospital between November 2021 and November 2022 were included in this study.

Electrochemical Performance of a Potassium-Ion Complex Gel Electrolyte with Succinonitrile Embedded in an In Situ Constructed 3D Polymer Framework.

Potassium-ion batteries (PIBs) represent an emerging energy storage technology, yet their widespread adoption is hindered by the inherent safety risks associated with conventional organic liquid electrolytes. To mitigate these challenges, a composite gel polymer electrolyte (SN@PPE-X) is synthesized by incorporating the plastic-crystal material succinonitrile (SN) into a rigid 3D confined polymer network. This is achieved through the in situ photopolymerization of pentaerythritol tetraacrylate (PETEA) after mixing SN with the monomer, forming a rigid framework that restricts SN mobility.

Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical Diagnoses.

In the evolving landscape of clinical Natural Language Generation (NLG), assessing abstractive text quality remains challenging, as existing methods often overlook generative task complexities. This work aimed to examine the current state of automated evaluation metrics in NLG in healthcare. To have a robust and well-validated baseline with which to examine the alignment of these metrics, we created a comprehensive human evaluation framework.

Effect of Probucol and Atorvastatin Treatment on the Self-Care Ability of Acute Cerebral Infarction Patients: A Randomized Clinical Trial Study.

Background: To investigate the effect of probucol and atorvastatin on self-care ability in patients with acute cerebral infarction (ACI).

Methods: Eighty-one patients with ACI admitted from November 2020 to May 2021 were divided into a combination treatment group (n = 40) and an atorvastatin group (n = 41). The atorvastatin group was given atorvastatin on the basis of conventional treatment, and the combination treatment group was treated with probucol and atorvastatin on the control basis.

Development and validation of the provider documentation summarization quality instrument for large language models.

Objectives: As large language models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments are essential to evaluate their performance before implementation and as models and documentation practices evolve. Existing instruments for provider documentation quality are often unsuitable for the complexities of LLM-generated text and lack validation on real-world data. The Provider Documentation Summarization Quality Instrument (PDSQI-9) was developed to evaluate LLM-generated clinical summaries.

Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge.

Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for healthcare providers to summarize and synthesize relevant details to their practice. To reduce cognitive load on providers, generative AI with Large Language Models have emerged to automatically summarize patient records into clear, actionable insights and offload the cognitive burden for providers. However, LLM summaries need to be precise and free from errors, making evaluations on the quality of the summaries necessary.

Long-Term Safety of "4-Hour" Hemoadsorption Combined with Hemodialysis in Maintenance Hemodialysis Patients: A Multicenter Prospective Cohort Study.

Introduction: Current guidelines recommend limiting hemoadsorption (HA) duration to 2 h during hemodialysis (HD) sessions due to theoretical concerns about adsorbent saturation and clotting risks. This multicenter prospective cohort study evaluated the long-term safety and efficacy of a novel "4Hs" protocol (4-h HA-HD with blood flow >250 mL/min).

Methods: 78 maintenance HD patients from four centers underwent 26 weeks of 4Hs therapy.

Current and future state of evaluation of large language models for medical summarization tasks.

Large Language Models have expanded the potential for clinical Natural Language Generation (NLG), presenting new opportunities to manage the vast amounts of medical text. However, their use in such high-stakes environments necessitate robust evaluation workflows. In this review, we investigated the current landscape of evaluation metrics for NLG in healthcare and proposed a future direction to address the resource constraints of expert human evaluation while balancing alignment with human judgments.

Comparative Time Efficiency of CT and MRI Multimodal Imaging Protocols in Acute Ischemic Stroke Evaluation.

Objective: To assess the time efficiency of computed tomography (CT) and magnetic resonance imaging (MRI) multimodal scanning protocols in the assessment of acute ischemic stroke (AIS), with potential implications for craniocerebral emergency management.

Methods: A retrospective analysis was conducted to assess the imaging workflows of CT and MRI for the assessment of AIS. The total examination time, derived from DICOM source data, encompassed pre-scan waiting periods, sequence acquisition times, and image reconstruction durations.

Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study.

Background: Electronic health records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives can overwhelm health care providers, increasing the risk of diagnostic inaccuracies. While large language models (LLMs) have showcased their potential in diverse language tasks, their application in health care must prioritize the minimization of diagnostic errors and the prevention of patient harm.

Alleviating effect of intranasal zinc on symptoms of allergic rhinitis.

Background: There is no information about the clinical implications and kinetics of zinc (Zn) in the nasal cavity, a center of allergic inflammation, and serum in subjects with allergic rhinitis (AR).

Objective: Effects of intranasal Zn on symptoms before and after allergen provocation were investigated in humans and mice with or without AR.

Methods: The first clinical follow-up study for Zn levels in nasal epithelial lining fluid (ELF) and serum was conducted in 57 control subjects and 44 patients with Japanese cedar pollinosis (JCP), a representative seasonal AR, from preseason to season.

Stabilization of RUNX1 Induced by O-GlcNAcylation Promotes PDGF-BB-Mediated Resistance to CDK4/6 Inhibitors in Breast Cancer.

Cyclin-dependent kinases 4 and 6 (CDK4/6) are crucial in regulating cell-cycle progression and cancer development. Targeting CDK4/6 has shown considerable promise in treating various cancers, including breast cancer. Despite significant therapeutic efficacy, resistance to CDK4/6 inhibitors (CDK4/6i), such as palbociclib, remains a substantial hurdle in clinical practice.

Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability.

Objective: To evaluate large language models (LLMs) for pre-test diagnostic probability estimation and compare their uncertainty estimation performance with a traditional machine learning classifier.

Materials And Methods: We assessed 2 instruction-tuned LLMs, Mistral-7B-Instruct and Llama3-70B-chat-hf, on predicting binary outcomes for Sepsis, Arrhythmia, and Congestive Heart Failure (CHF) using electronic health record (EHR) data from 660 patients. Three uncertainty estimation methods-Verbalized Confidence, Token Logits, and LLM Embedding+XGB-were compared against an eXtreme Gradient Boosting (XGB) classifier trained on raw EHR data.

Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies.

Objectives: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sources. However, there are many parameters to optimize in just the retrieval system alone.

Quantitative evaluation of pancreatic neuroendocrine tumors utilizing dual-source CT perfusion imaging.

Objective: We aimed to quantitatively analyze the perfusion characteristics of pancreatic neuroendocrine tumors (pNETs) utilizing dual-source CT imaging.

Methods: Dual-source CT perfusion scans were obtained from patients with pNETs confirmed by surgical or biopsy pathology. Perfusion parameters, including blood flow (BF), blood volume (BV), capillary permeability surface (PS), mean transit time (MTT), contrast transit time to the start (TTS), and contrast transit time to the peak (TTP), were statistically analyzed and compared with nearby healthy tissue.

LCD benchmark: long clinical document benchmark on mortality prediction for language models.

Objectives: The application of natural language processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent.

Association Between Blood-Brain Barrier Disruption and Stroke-Associated Pneumonia in Acute Ischemic Stroke Patients After Endovascular Therapy: A Retrospective Cohort Study.

Background: Stroke, particularly due to large vessel occlusion (LVO), is a major cause of mortality and disability globally. Endovascular therapy (ET) significantly improves outcomes for acute ischemic stroke (AIS) patients, but complications such as stroke-associated pneumonia (SAP) increase mortality and healthcare costs. This study investigates the association between blood-brain barrier (BBB) disruption and the increased risk of SAP and explores the relationship between BBB disruption and medium-term functional outcomes.

Prompt engineering with a large language model to assist providers in responding to patient inquiries: a real-time implementation in the electronic health record.

Background: Large language models (LLMs) can assist providers in drafting responses to patient inquiries. We examined a prompt engineering strategy to draft responses for providers in the electronic health record. The aim was to evaluate the change in usability after prompt engineering.

On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models.

Objective: Traditional knowledge-based and machine learning diagnostic decision support systems have benefited from integrating the medical domain knowledge encoded in the Unified Medical Language System (UMLS). The emergence of Large Language Models (LLMs) to supplant traditional systems poses questions of the quality and extent of the medical knowledge in the models' internal knowledge representations and the need for external knowledge sources. The objective of this study is three-fold: to probe the diagnosis-related medical knowledge of popular LLMs, to examine the benefit of providing the UMLS knowledge to LLMs (grounding the diagnosis predictions), and to evaluate the correlations between human judgments and the UMLS-based metrics for generations by LLMs.

Sequential severe immune-related adverse events induced by PD-1 inhibitor: a case report and literature review.

In a variety of cancers, immune checkpoint inhibitors (ICIs) have demonstrated substantial survival advantages. Nevertheless, the widespread use of ICIs in the clinic has resulted in a growing interest in immune-related adverse events (irAEs) and their treatment methods. This paper reports a case in which a patient with three sequential severe irAEs was successfully treated.