With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

In observational research,a primary objective is to accurately and reliably assess the causal impact of exposure on outcomes.Identifying and properly adjusting for confounding factors is a key prerequisite and central challenge to achieving this goal.Ineffective management of confounders,whether by neglecting significant ones,(leading to residual confounding),or by over-adjusting for irrelevant factors,(introducing collider bias),can distort effect estimates and lead to erroneous scientific conclusions and clinical decisions.Therefore,it is essential to develop and implement systematic,transparent,and reproducible methods for identifying and selecting confounding factors to enhance the validity and reliability of causal inferences in observational studies.This paper provides a systematic review of directed acyclic graphs(DAGs),a robust visual causal modeling tool,and offers a detailed examination of three prominent criteria for selecting confounding factors based on DAGs:the Pre-exposure criterion,the Common cause criterion,and the Modified disjunctive cause criterion.The aim is to equip researchers with a structured and theoretically grounded framework for identifying and selecting confounding factors,thereby improving the process of estimating causal effects in observational studies.

In observational research,a primary objective is to accurately and reliably assess the causal impact of exposure on outcomes.Identifying and properly adjusting for confounding factors is a key prerequisite and central challenge to achieving this goal.Ineffective management of confounders,whether by neglecting significant ones,(leading to residual confounding),or by over-adjusting for irrelevant factors,(introducing collider bias),can distort effect estimates and lead to erroneous scientific conclusions and clinical decisions.Therefore,it is essential to develop and implement systematic,transparent,and reproducible methods for identifying and selecting confounding factors to enhance the validity and reliability of causal inferences in observational studies.This paper provides a systematic review of directed acyclic graphs(DAGs),a robust visual causal modeling tool,and offers a detailed examination of three prominent criteria for selecting confounding factors based on DAGs:the Pre-exposure criterion,the Common cause criterion,and the Modified disjunctive cause criterion.The aim is to equip researchers with a structured and theoretically grounded framework for identifying and selecting confounding factors,thereby improving the process of estimating causal effects in observational studies.

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.

As the volume of medical research using large language models (LLM) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published statement titled by TRIPOD-LLM reporting guideline for studies using large language models. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLM. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLM into healthcare.

Performance appraisal of public hospitals have given a guidance for the development of public hospitals at all levels.A Class A tertiary hospital reviewed the problems in the development of the hospital at the present stage and focused on the following four aspects:①insufficient fine management;②No clear orientation of discipline development;③The bottleneck of the improvement of medical operation efficiency;④New challenges in the reform of payment mode.The tertiary hospital launched a fine management practice in May 2022,in order to solve the problems by taking the Department of Surgery as a pilot area,laying the foundation for fine management through information system construction,improving the efficiency of medical operation through management process optimization,improving the overall competitiveness of disciplines through the construction of sub-specialty and Discipline Alliance and adjusting the performance appraisal index system to play the role of performance incentives.The measures effectively improve the overall capacity and efficiency of hospital medical services and help the hospital to achieve high-quality development.

Objective In order to reasonably reduce QY group and improve the accuracy of DRG,we analyzed the data of QY(ambiguity)group in the Diagnosis Related Groups in urology department of a three A and tertiary hospital.Methods Medical insurance settlement statements of urology department of a hospital in the DRG sub-platform of a municipal medical insur-ance bureau were collected from April 1,2022 to November 30,2022,extracting QY cases.The medical records of QY cases were extracted from the digital medical record browser of a hospital.Senior quality control physicians were organized to check medical records,disease classification and medical insurance settlement statements of QY group cases,analyzing the cases.Excel 2010 was used for statistical analysis of the data.Results ① 2 810 copies of medical insurance settlement statements were obtained,of which 43 cases were in the QY groups,accounting for 1.53％.②The cases mainly distributed in urinary system tumors(27.91％),neurogenic bladder(18.60％),prostatic hyperplasia(16.28％),ureteral diseases(11.62％),and ure-thral diseases(11.62％).(3)The total hospitalization cost of patients in QY groups was 731 684.1 yuan,and the average hospi-talization cost was 17 015.91 yuan.The total payment amount of DRG was 419 807.24 yuan,and the average payment amount was 9 762.96 yuan.④The causes of QY groups were as follows:There were 14 cases of DRG grouping rule defect,accounting for 32.56％;There were 29 cases of hospital management defects,accounting for 67.44％.Among the hospital management de-fects,13 cases(30.23％)of the medical insurance settlement statements were defective;There were 8 cases(18.60％)of er-rors in doctors'major diagnoses and major surgical operations.There were 8 cases(18.60％)of coding errors in major diagno-ses and major surgical operations.Conclusion To reasonable reduction QY group,it is necessary to optimize DRG grouping rules and strengthen hospital internal management.Only by paying attention to improve the connotation quality of medical re-cords,accurately classify disease and surgical name,strengthen the management and review of medical insurance settlement state-ments,and establish a long-term mechanism for DRG data quality control,we can further improve the accuracy of DRG and ac-tively promote the reform of medical insurance payment.

OBJECTIVE To evaluate the effectiveness， safety， economy， innovation， suitability and accessibility of recombinant Mycobacterium tuberculosis fusion protein （EC）， and to provide evidence for selecting skin detection methods for tuberculosis infection diagnosis and auxiliary diagnosis of tuberculosis. METHODS The effectiveness and safety of EC compared with purified protein derivative of tuberculin （TB-PPD） were analyzed by the method of systematic review. Cost minimization analysis， cost-effectiveness analysis and cost-utility analysis were used to evaluate the short-term economy of EC compared with TB-PPD， and cost-utility analysis was used to evaluate the long-term economy. The evaluation dimensions of innovation， suitability and accessibility were determined by systematic review and improved Delphi expert consultation， and the comprehensive score of EC and TB-PPD in each dimension were calculated by the weight of each indicator. RESULTS The scores of effectiveness， safety， economy， innovation and suitability of EC were all higher than those of TB-PPD. The affordability scores of the two drugs were consistent， while the availability score of EC was lower than those of TB-PPD. After considering dimensions and index weight， the scores of effectiveness， safety， economy， innovation， suitability， accessibility and the comprehensive score of EC were all higher than those of TB-PPD. CONCLUSIONS Compared with TB-PPD， EC performs better in all dimensions of effectiveness， safety， economy， innovation， suitability and accessibility. However， it is worth noting that EC should further improve its availability in the dimension of accessibility.

The umbrella trial has received increasing attention in the design of clinical trials for oncology drugs in recent years. This trial design categorizes a single disease into multiple sub-types based on predictive biomarkers or other predictive factors, and simultaneously evaluates the efficacy of multiple targeted therapies. When compared with the traditional drug development model of phase Ⅰ, phaseⅡ, and phase Ⅲ randomized controlled trials, umbrella trials are a more scientifically rigorous trial design that can speed up drug evaluation to address the conflict between numerous untested drugs and diseases with a lack of effective treatment options. This article will focus on the concept, main characteristics, eligibility criteria, design and statistical considerations, ethical considerations, and future directions of umbrella trials, with the aim of providing methodological guidance for the design of clinical trials for oncology drugs.

Background::Although existing mycological tests (bronchoalveolar lavage [BAL] galactomannan [GM], serum GM, serum (1,3)-β-D-glucan [BDG], and fungal culture) are widely used for diagnosing invasive pulmonary aspergillosis (IPA) in non-hematological patients with respiratory diseases, their clinical utility in this large population is actually unclear. We aimed to resolve this clinical uncertainty by evaluating the diagnostic accuracy and utility of existing tests and explore the efficacy of novel sputum-based Aspergillus assays. Methods::Existing tests were assessed in a prospective and consecutive cohort of patients with respiratory diseases in West China Hospital between 2016 and 2019 while novel sputum assays (especially sputum GM and Aspergillus-specific lateral-flow device [LFD]) in a case-controlled subcohort. IPA was defined according to the modified European Organization for Research and Treatment of Cancer/Mycoses Study Group criteria. Sensitivity and specificity were computed for each test and receiver operating characteristic (ROC) curve analysis was performed. Results::The entire cohort included 3530 admissions (proven/probable IPA = 66, no IPA = 3464) and the subcohort included 127 admissions (proven/probable IPA = 38, no IPA = 89). Sensitivity of BAL GM (≥1.0 optical density index [ODI]: 86% [24/28]) was substantially higher than that of serum GM (≥0.5 ODI: 38% [39/102]) ( χ2 = 19.83, P < 0.001), serum BDG (≥70 pg/mL: 33% [31/95]) ( χ2 = 24.65, P < 0.001), and fungal culture (33% [84/253]) ( χ2 = 29.38, P < 0.001). Specificity varied between BAL GM (≥1.0 ODI: 94% [377/402]), serum GM (≥0.5 ODI: 95% [2130/2248]), BDG (89% [1878/2106]), and culture (98% [4936/5055]). Sputum GM (≥2.0 ODI) had similar sensitivity (84% [32/38]) (Fisher’s exact P = 1.000) to and slightly lower specificity (87% [77/89]) ( χ2 = 5.52, P = 0.019) than BAL GM (≥1.0 ODI). Area under the ROC curve values were comparable between sputum GM (0.883 [0.812-0.953]) and BAL GM (0.901 [0.824-0.977]) ( P = 0.734). Sputum LFD had similar specificity (91% [81/89]) ( χ2 = 0.89, P = 0.345) to and lower sensitivity (63% [24/38]) ( χ2 = 4.14, P = 0.042) than BAL GM (≥1.0 ODI), but significantly higher sensitivity than serum GM (≥0.5 ODI) ( χ2 = 6.95, P = 0.008), BDG ( χ2 = 10.43, P = 0.001), and fungal culture ( χ2 = 12.70, P < 0.001). Conclusions::Serum GM, serum BDG, and fungal culture lack sufficient sensitivity for diagnosing IPA in respiratory patients. Sputum GM and LFD assays hold promise as rapid, sensitive, and non-invasive alternatives to the BAL GM test.