OBJECTIVES:To systematically evaluate the performance of generative artificial intelligence (GenAI) models, DeepSeek-V3 and the Qwen3 series, in the differential diagnosis of weight loss. METHODS:A search was conducted in the PubMed database for all case reports published in the American Journal of Case Reports between January 1, 2012 and June 2, 2025, containing the term "weight loss" in the title or abstract. Two senior general practitioners independently reviewed each case to determine whether it met predefined diagnostic criteria for weight loss (emaciation). Cases that did not meet these criteria, had incomplete information, or involved clearly defined specialty-specific diagnoses and treatments were excluded. The remaining cases were then compiled into standardized clinical case summaries. These summaries were presented to DeepSeek-V3 and the Qwen3 series models (Qwen3-235B-A22B, Qwen3-30B-A3B, and Qwen3-32B) to generate ranked lists of the top 10 differential diagnoses. The models were not specifically fine-tuned for this task. Sensitivity, precision, and F1-score were used to evaluate performance. Intergroup comparisons were performed using McNemar's test and Cochran's Q test. RESULTS:A total of 87 case were analyzed. DeepSeek-V3 demonstrated better performance than Qwen3-235B-A22B in sensitivity, precision, and F1-score, especially at the Top5 level (P=0.043). Among the Qwen3 series models, Qwen3-235B-A22B showed the best performance in sensitivity, precision, and F1-score for the Top1 diagnosis, but the differences among the three Qwen3 models across all diagnostic levels were not statistically significant (all P>0.05). CONCLUSIONS:Domestic GenAI models exhibit a characteristic of "breadth over precision" in the differential diagnosis of weight loss, with DeepSeek-V3 performing better at key diagnostic levels. Although the sensitivity and precision for the top-ranked diagnosis require improvement, these models have the potential to serve as effective clinical decision support tools, broadening the diagnostic perspectives of general practitioners.

General practitioners (GPs) play a dual role in influenza prevention and control as both medical decision-makers and a high-risk population for infection. This study, based on the Reasoned Action Approach (RAA), aimed to explore GPs' influenza vaccination willingness and its influencing factors. Data were collected via an online questionnaire from February 11 to March 20, 2025, using convenience sampling. The questionnaire covered general profiles, influenza vaccination in the last season, vaccination willingness, and a RAA-based scales (including dimensions of attitude, subjective norms, and perceived behavioral control), and information-seeking behavior (active information collection, methods, and trusted sources). Among 341 questionnaires distributed, 330 valid ones were returned. The mean vaccination willingness score was 3.82 out of 5. Multiple regression analysis showed that vaccination willingness was positively associated with subjective norms (β = 0.367), attitudes (β = 0.291), vaccination in the last season (β = 0.124), and type of workplace (β = 0.111) (all p < .05). Given GPs' exemplary role as health promoters, this study proposed multidimensional strategies like strengthening their health education, establishing social norm guidance mechanisms, and optimizing vaccine service accessibility. These aimed to enhance GPs' vaccination willingness and their positive guidance on preventive behaviors in the population.

Objectives:To understand GPs' (General Practitioners') knowledge, attitudes, and practices concerning inhalation education for COPD patients; to pinpoint key discrepancies between knowledge, attitude, and behavior; to offer evidence for designing multi-level targeted training and implementation strategies that can enhance the quality of inhaler education provided at the grassroots level. Methods:A cross-sectional, web-based study was conducted among GPs in Yiwu, Zhejiang Province, China, from November to December 2025. A validated, self-administered questionnaire assessed KAP across three dimensions (knowledge, attitude, practice). Descriptive statistics, Kruskal-Wallis H test, correlation analysis, and multiple regression were used for data analysis. Results:Among the 213 participating GPs, mean standardized scores (0-100) were 31.2 ± 9.0 for knowledge, 84.1 ± 12.5 for attitude, and 41.0 ± 15.4 for practice. Lower knowledge scores were observed among GPs with junior professional titles (28.4 ± 9.0) and those with less than 5 years of clinical experience (27.1 ± 9.1). Multivariate regression showed that knowledge was independently associated with practice behavior (standardized β = 0.508, P < 0.001). Conclusion:This study indicates that GPs in Yiwu showed positive attitudes but insufficient knowledge and practice in COPD inhaler education, with a clear attitude-practice gap. These findings may support the development of targeted strategies to improve GPs' engagement in inhaler technique education for COPD patients.

Fatigue is a highly prevalent but understudied health complaint among college students. This nationwide study aimed to estimate the prevalence, severity distribution, and lifestyle determinants of fatigue among Chinese college students using a validated multidimensional instrument. A geographically stratified cross-sectional survey was conducted across 30 Chinese provinces. Full-time college students completed an anonymous online questionnaire including the Brief Fatigue Inventory (BFI), sociodemographic characteristics, lifestyle behaviors, and self-reported disease history. Hierarchical multiple linear regression was used to identify independent predictors of BFI Total Score. A total of 3,014 valid responses were analyzed (63.9

Background:Sarcopenia, an age-related syndrome marked by progressive loss of skeletal muscle mass and function, is associated with frailty, disability, falls, and increased mortality among older adults. However, existing diagnostic methods, such as dual-energy X-ray absorptiometry (DXA) and physical performance tests, are often inaccessible in routine clinical practice due to equipment and time constraints. Objective:This study aimed to develop and validated a multimodal, explainable AI model for identifying sarcopenia using routinely available chest CT scans and outpatient clinical data in older adults. Methods:A total of 290 participants (mean age 67.6 ± 5.8 years; 38.9% female) were included. A weakly supervised segmentation framework combining the Segment Anything Model (SAM) and Contrastive Language-Image Pretraining (CLIP) was employed to extract muscle features at the T12 level. Clinical variables, including anthropometric indices, lifestyle behaviors, and biochemical markers, were encoded and fused with imaging-derived features. A multi-layer perceptron (MLP) was trained to classify sarcopenia based on 2019 AWGS criteria. Model interpretability was assessed using SHAP (Shapley Additive Explanations) values. Results:The model achieved an AUC of 0.88 (95% CI: 0.83-0.92), accuracy of 0.85 (95% CI 0.82-0.89), sensitivity of 0.79 (95% CI: 0.70-0.987), and specificity of 0.88 (95% CI: 0.83-0.92). SHAP analysis revealed that gender, total triiodothyronine, creatine kinase, body mass index and creatinine were the most influential predictors. The fusion of weakly supervised learning and multimodal data enabled effective muscle region segmentation and improved diagnostic performance. Conclusion:In summary, we developed and internally validated an explainable multimodal AI model that integrates chest CT-derived muscle features with routine outpatient clinical variables for sarcopenia detection in older adults. The model demonstrated strong diagnostic performance and interpretability, highlighting its potential for opportunistic risk stratification in routine clinical workflows. Future multi-center validation and prospective studies are warranted to confirm its generalizability and long-term clinical utility.

BACKGROUND:Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS:A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS:A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION:BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Metastatic hepatocellular carcinoma (HCC) seriously threatens patients' prognosis. It was previously suggested that the insulin growth factor binding protein (IGFBP) family could serve as cancer suppressors in the development and metastasis of HCC. However, the role of IGFBP4 and its underlying molecular mechanism in HCC metastasis is elusive. In the present study, it was found that IGFBP4 is significantly downregulated in HCC, whose expression is positively correlated with the prognosis of patients with HCC. Overexpression of IGFBP4 restrained migration abilities and cancer metastasis of HCC cells both in vitro and in vivo. Furthermore, it was found that IGFBP4 represses HCC metastasis by inhibiting epithelial-mesenchymal transition. Molecular mechanism studies showed that overexpression of IGFBP4 obviously suppresses NOTCH1 signaling in HCC. As for the upstream regulatory mechanism, it was revealed that downregulation of IGFBP4 in HCC was caused by CpG islands' hyper-methylation-dependent degradation mediated by MYBBP1A. Inhibition of MYBBP1A limited HCC metastatic ability and silence of IGFBP4 at the same time restored HCC metastatic potentials. Clinical data demonstrated that low expression of IGFBP4 was found in patients with HCC, especially with lymphatic metastasis. High MYBBP1A expression and low IGFBP4 expression in HCC were correlated with poor survival of patients with HCC. Summarily, in the present study, it was revealed that MYBBP1A/IGFBP4/NOTCH1 pathway could play a crucial role in the progression and metastasis of HCC, which stimulates novel therapeutic and diagnostic strategies against metastatic HCC.

OBJECTIVES:Inflammation is intricately linked to the emergence and advancement of most cancers, playing a pivotal role in their malignant transformation. Observational evidence revealed the role of cytokines in pancreatic cancer (PC) carcinogenesis. However, observational studies may be limited by small sample sizes, confounding factors, and reverse causality when establishing a correlation between inflammatory cytokines and PC risk. DESIGN:Conducting a two-sample Mendelian randomization analysis, we investigated the potential relationship between inflammatory cytokines in circulation and pancreatic cancer. Data from the most extensive genome-wide association studies (GWAS) on cytokines were utilized, involving 31 112 individuals of European descent. Additionally, the PC GWAS from the Integrative Epidemiology Unit (IEU) analysis of Finnish Biobank data was included, consisting of 605 PC cases and 218 187 controls of European ancestry. RESULTS:Around 47 cytokines were systematically screened, which revealed that circulating levels of IL-1ra (OR: 0.63; 95% CIs: 0.46-0.87; P-value: 4.9 × 10-4), IP-10 (OR: 0.33; 95% CIs: 0.18-0.59; P-value: 1.8 × 10-4) and macrophage inflammatory protein (MIP)-1a (OR: 1.37; 95% CIs: 1.08-1.75; P-value: 1 × 10-2) predicted by genetic criteria were prominently linked to an elevated risk of overall PC. CONCLUSION:Further evidence indicates that certain inflammatory cytokines play critical roles in PC carcinogenesis and that specific inflammatory cytokines can be targeted to prevent PC. Nevertheless, additional research is necessary to assess the potential of these cytokines in detecting PC at an early stage.

This study investigated videoconferencing fatigue and its influencing factors among Chinese people to inform interventions for reducing fatigue and enhancing well-being during COVID-19 and post-pandemic times. Data were collected via an online questionnaire from February 7 to 13, 2023, using convenience sampling. The questionnaire covered general profiles, health status, videoconferencing usage and intensity, and five nonverbal mechanisms, along with the Zoom Exhaustion and Fatigue scale, which assessed the main indicators. Univariate and step-wise multiple linear regression analyses were used to identify influencing factors. Of the 293 participants, 291 completed the survey (99.32%). The mean Zoom Exhaustion and Fatigue score was 2.18 out of 5. Factors significantly associated with increased fatigue included greater technological complexity, worse health status, more negative attitudes, higher levels of mirror anxiety and sense of feeling physically trapped, and higher cognitive load associated with producing nonverbal cues. Given the widespread use of videoconferencing tools, it is essential to understand the variables that influence videoconferencing fatigue thoroughly and to take the necessary steps to reduce it. According to the results of the study, recommendations to reduce fatigue include familiarizing with software, occasionally turning off cameras and microphones, changing posture, maintaining positive attitudes, reducing unnecessary meetings, and increasing break duration and frequency.

Although ordering food online has become a leading lifestyle factor among the Chinese urban population, its health impact is understudied. This study aims to examine this aspect, focusing on online takeaway food impacts related to foodborne illness, nutrition, and suboptimal health (i.e., an intermediate status between being healthy and diagnosed with a disease) on consumers. A snowball sampling method was adopted, targeting urban consumers aged 18–59 across mainland China by posing an e-questionnaire survey. Information collected includes demographic data, foodborne illness occurrence, nutrition and suboptimal health impacts when ordering online takeaway foods. The questionnaire was adapted from the Diet Quality Questionnaire and Sub-Health Measurement Scale version 1.0. Descriptive statistical methods, Mann‒Whitney test, and Spearman’s correlation test were employed to analyse the data. A total of 610 questionnaires were included. Consumers ordering online takeaway foods at least once per week (hereon termed as ‘frequent consumers’) often experienced fake reviews (50.71

BACKGROUND:The Global Leadership Initiative on Malnutrition (GLIM) criteria have been validated in various clinical settings since 2018, but prospective validation in patients with congestive heart failure (CHF) who are hospitalized remains limited. This study compares the prognostic performance of the GLIM criteria and Mini-Nutritional Assessment (MNA)-defined malnutrition for all-cause mortality in CHF patients and explores the strongest predictive indicator within the GLIM criteria. METHODS:This single-center prospective cohort study included inpatients with CHF. Agreement between the GLIM criteria and MNA was assessed using Cohen κ coefficient. Survival data were analyzed using Kaplan-Meier curves and adjusted Cox regression analyses. RESULTS:Among 498 CHF inpatients, 84 (16.9%) died during the 18-month follow-up. Malnutrition prevalence was 47.2% and 50.4% based on the GLIM criteria and MNA, respectively (κ = 0.68; P < 0.001). Malnutrition was independently associated with a higher risk of all-cause mortality (GLIM criteria: hazard ratio, 2.16 [95% confidence interval (CI), 1.13-4.13]; MNA: hazard ratio, 4.28 [95% CI, 1.98-9.22]). Low body mass index was the strongest predictor of all-cause mortality in multivariable analysis (hazard ratio, 5.14; 95% CI, 3.19-8.27). CONCLUSION:The GLIM criteria showed strong consistency with MNA and effectively predicted all-cause mortality in CHF patients within 18 months.

Background Heart failure (HF) and diabetes mellitus (DM) frequently coexist, exacerbating disease progression and increasing hospital readmission risk. Accurate prediction of readmission in HF patients with DM remains a clinical challenge. This study aims to develop and validate a machine learning (ML)-based model incorporating inflammatory and metabolic biomarkers to enhance risk stratification. Methods This retrospective cohort study included HF patients with DM hospitalized between January 2020 and February 2024. A total of 716 patients were randomly divided into training (70 %) and validation (30 %) sets. Seven ML models were developed using clinical parameters, inflammatory markers, and metabolic indices. Model performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), calibration, sensitivity, specificity, and Brier score, among others. External validation was conducted using an independent cohort of 687 patients. SHapley Additive Explanations (SHAP) analysis was applied for model interpretability, and a web-based dynamic nomogram was developed for clinical implementation. Results Among 716 patients, 256 (35.8 %) were readmitted within one year. The random forest (RF) model demonstrated superior performance (AUC = 0.87, Brier score = 0.151), outperforming other ML models. External validation confirmed its generalizability (AUC = 0.82). SHAP analysis identified age, brain natriuretic peptide (BNP), New York Heart Association (NYHA) class, HF classification, and triglyceride-glucose body mass index (TYG-BMI) as key predictors. The dynamic nomogram provided individualized risk predictions, enhancing clinical applicability. Conclusions This study developed an ML-based model integrating inflammatory and metabolic biomarkers for predicting readmission in HF patients with DM. The model demonstrated robust performance and interpretability, showing potential as a supportive tool for early risk identification and personalized risk communication in clinical settings.

Objective: Large Language Models (LLMs) have demonstrated strong capabilities in medical text understanding and generation. However, their trustworthiness in diagnosis-oriented medical tasks remains constrained by the lack of structured guidance on how clinically relevant diagnostic evidence is internally attended to and utilized during model learning. Method: We propose an Etiology-Aware Attention Supervision framework that introduces structured etiological information as an external supervisory signal for training large language models. Specifically, we construct Clinical Etiology Schema (CES) derived from authoritative clinical guidelines for three acute abdominal conditions: acute appendicitis, acute pancreatitis, and acute cholecystitis. Based on CES annotations, we develop an Etiology-Aware Head Identification strategy to identify attention heads that consistently align with etiological evidence. Building on this analysis, we design a structure-guided parameter-efficient fine-tuning approach that steers attention distributions toward clinically relevant evidence through an additional supervision loss, without modifying the base model architecture. Result: Experiments conducted on a Consistent Diagnosis Cohort demonstrate that the proposed framework improves average diagnostic accuracy by 15.65

General practice in China has developed rapidly in recent years with remarkable progress,but the gap with developed countries in Europe and the United States is still large,and the training system of general practitioners still remains impefect.In Germany,the construction of the primary health care system and the training system of general practitioners have been well developed.Under the system of universal health insurance coverage and hierarchical diagnosis and treatment,a high level of health and patient satisfaction with primary healthcare services among residents have been achieved in Germany.Therefore,this study compares post-graduate education and continuing education of general practice in China and Germany,analyzes the challenges of general practice education reform in China,drawing on the conceptual framework of general practice education in Germany,and proposes targeted ideas and recommendations for solutions as follows:for the standardized residency training of general practice,increase the rotation flexibility as appropriate to facilitate the optimization of trainees'individualized competencies,incorporate the standardized curriculum of psychosomatic medicine and Balint group training to improve trainees'competence in psychosomatic medicine,establish standardized selection criteria and promote standardized training program for faculty of community hospital,and revisit the duration of general practice(including community)rotation after improving the qualifications of general practice faculty of community hospital;for the continuing education,incorporate the special interest and small specialties into the general practice continuing education system to strengthen the functional medical characteristics of general practice and promote the professional diversification of general practitioners,and establish a national unified platform for continuing education in general practice.More practical research and resources are needed to improve the training system of general practitioners in China in the future.

口臭是临床常见疾病，严重影响患者的社交和心理健康。全科医生在口臭的诊疗中扮演着关键角色。本文通过文献回顾，提出了一套全科医学视角下的口臭诊疗思路，包括病史询问、体格检查、检测方法和治疗策略。本文强调了个性化诊疗的重要性，并指出全科医生需提高对口臭患者的关注和管理。

Background: Heart failure with preserved ejection fraction (HFpEF) is associated with elevated rates of readmission and mortality. Accurate prediction of readmission risk is essential for optimizing healthcare resources and enhancing patient outcomes. Methods: We conducted a retrospective cohort study utilizing HFpEF patient data from two institutions: the First Affiliated Hospital Zhejiang University School of Medicine for model development and internal validation, and the Affiliated Hospital of Xuzhou Medical University for external validation. A machine learning (ML) model was developed and validated using 53 variables to predict the risk of readmission within one year. The model's performance was assessed using several metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, model training time, model prediction time and brier score. SHAP (SHapley Additive exPlanations) analysis was employed to enhance model interpretability, and a dynamic nomogram was constructed to visualize the predictive model. Results: Among the 766 HFpEF patients included in the study, 203 (26.5%) were readmitted within one year. The LightGBM model exhibited the highest predictive performance, with an AUC of 0.88 (95% confidence interval (CI):0.84-0.91), an accuracy of 0.79, a sensitivity of 0.81, and a specificity of 0.78. Key predictors included the E/e' ratio, NYHA classification, LVEF, age, BNP levels, MLR, history of atrial fibrillation (AF), use of ACEI/ARB/ ARNI, and history of myocardial infarction (MI). External validation also demonstrated strong predictive performance, with an AUC of 0.87 (95%CI:0.83-0.91). Conclusions: The LightGBM model exhibited robust performance in predicting one-year readmission risk among HFpEF patients, providing a valuable tool for clinicians to identify high-risk individuals and implement timely interventions.

General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients' electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0.7873, recall@10 of 0.9020 and hits@10 of 0.9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.

Objective The primary aim of this study is to analyze health information seeking behaviors of users related to child fever within online health communities. The findings will serve as a foundation for the development of targeted interventions and resources for addressing the specific information needs related to child fever. Ultimately, this will enhance parental capabilities in managing fever in children and for improving the quality of communication between healthcare professionals and parents dealing with feverish children.Methods This study employed data crawling to gather Q&A data on childhood fever from online health communities, specifically "haodf.com" between March 15, 2022, and March 15, 2023. A total of 47,781 texts were analyzed using a mixed research approach that combines qualitative text topic analysis with BERTopic algorithm.Results The health information needs regarding children's fever can be categorized into 6 primary topics and 17 secondary topics. Among them, parents' demand for medication consultation and medical guidance (Topic A) was the highest at 45.40%, followed by information concerning the management of fever symptoms and body temperature in children (Topic B) at 30.35%. 13.24% of the data focused on examination recommendations and interpretation of results (Topic C).Conclusions This study proposes a mixed thematic analysis method combining qualitative text thematic analysis and the BERTopic topic model, which reveals parents' information-seeking behaviors about children with fever. It emphasizes the challenges faced by parents in assessing their children's condition and highlights the necessity of continuous health information support and evidence-based medical knowledge. This can promote the improvement of medical services, optimize doctor-patient communication, strengthen patient information support, and optimize the content of online health communities.

Background Rational use of antimicrobial drugs play an important role in improving clinical efficacy. However, inappropriate antibiotic use accelerates antimicrobial resistance, We aimed to assess the general practitioner's understanding of the use of antibiotics. Methods A self-designed online questionnaire was conducted among general practitioners. The content includes basic information, general practitioners' understanding of the use of antibiotics,training needs for general practitioners (GPs) on use of antibiotics. Results A total of 772 GPs participated in the survey,with an average age of 40.97±8.87 years, of which 46.63%(360) were male.724（93.78%）GPs agree“High rates of antibiotic use could lead to bacterial resistanc”.657（85.10%）GPs agree“A serious problem of antibiotic abuse in China”.711（92.10%）GPs agree “Abuse of antibiotics is one of the main reasons for bacterial resistance”.661(85.62%) GPs agree“Need fast and effective diagnostic techniques to assist me in using antibiotics”. 561(72.67%) GPs often promote rational medication knowledge to patients, 582(75.39%) GPs often study adverse reactions of drugs and inform to patients.496(64.25%) GPs often actively learn about antibacterial drug related knowledges, 424(54.92%) GPs often require clinical or laboratory evidence of bacterial infections when using antibiotics. On average, 247（32.05%） GPs master in the use of antibiotics, and 364（47.18%） GPs indicate that they are familiar with the use of antibiotics. On average, 627(81.21%) GPs have chosen appropriate answers regarding the use of antibiotics. 754(97.67%) GPs consider it was necessary to participate in training on the rational use of antibiotics, and 745(96.51) GPs have training needs. 713(92.36%) GPs believe that clinical pharmacist intervention is necessary when using antibiotics. Conclusions The rational application of antibiotics is the key to improving efficacy. Most GPs have a good understanding of when to use antibiotics and the plan for using antibiotics, but there are significant differences on some issues.More than 90% of GPs believe that training on antibiotics and intervention from clinical pharmacists are necessary. So, it is recommended to strengthen the training of general practitioners, based on evidence-based medicine, strictly grasp the indications for the use of antibiotics, strive for targeted treatment, reduce experiential treatment, and ensure that the indications, variety selection, administration route, dosage, and course of treatment for the use of antibiotics are suitable for patients.

General practitioners are trained to care for patients with a high level of responsibility and professional competency. However, there are few reports on the physical and mental health status of general practitioners (GPs) in China, particularly regarding help seeking and self-treatment. The primary aims of this study were to explore GPs’ expectations of their own family doctors and their reflection on role positioning, and to explore the objective factors that hinder the system of family doctors. Cross-sectional study. We conducted an online survey of Chinese GPs. Descriptive statistics were used to summarize the findings. More than half of the participants (57.20