Objective To investigate the clinical efficacy of Xiaozhi Tea(composed of Eupatorii Herba,Nelumbinis Folium,Chrysanthemi Flos,Cassiae Semen,Crataegi Fructus,bran-fried Atractylodis Macrocephalae Rhizoma,Poria,Pseudostellariae Radix,Citri Reticulatae Pericarpium,Glycyrrhizae Radix et Rhizoma Praeparata cum Melle)combined with Atorvastatin Calcium Tablets for the treatment of hyperlipidemia patients with turbid phlegm obstruction syndrome.Methods A retrospective cohort study was conducted in 200 hyperlipidemia patients with turbid phlegm obstruction syndrome who visited the outpatient department of Shenzhen Bao'an Traditional Chinese Medicine Hospital Group from September 2023 to September 2024.The patients were equally divided into a trial group and a control group based on the treatment regimen,with 100 cases in each group.The control group received oral use of Atorvastatin Calcium Tablets alone,while the trial group received Xiaozhi Tea in addition to Atorvastatin Calcium Tablets orally,both groups were treated for 8 weeks.Changes in traditional Chinese medicine(TCM)syndrome scores and lipid profiles of total cholesterol(TC),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),and low-density lipoprotein cholesterol(LDL-C)in the two groups were observed before and after treatment.After treatment,the clinical efficacy and safety of the two groups were evaluated.Results(1)There were 3 patients in the control group dropping out due to lack of follow-up data,leaving 197 patients who eventually completed the study,100 cases in the trial group and 97 cases in the control group.(2)After 8 weeks of treatment,the total effective rate in the trial group was 97.00%(97/100)and that in the control group was 87.63%(85/97).The intergroup comparison(tested by chi-square test)showed that the trial group showed significantly stronger efficacy than the control group(P＜0.05).(3)Both groups exhibited significant reductions in TCM syndrome scores after treatment in comparison with those before treatment(P＜0.05),and a more pronounced reduction was presented in the trial group(P＜0.05).(4)Both groups showed decreased TC,TG,and LDL-C levels(P＜0.05)and increased HDL-C level after treatment in comparison with those before treatment(P＜0.05).The trial group demonstrated more obvious reduction of TC,TG,LDL-C,and more obvious elevation of HDL-C than the control group(P＜0.05).(5)In terms of safety,no severe adverse reactions occurred in either group.The incidence of adverse reactions in the trial group was 1.00%(1/100)and that in the control group was 2.06%(2/97),with no statistically significant difference between groups(P＞0.05).Conclusion Xiaozhi Tea combined with Atorvastatin Calcium Tablets exerts certain efficacy in treating hyperlipidemia with turbid phlegm obstruction syndrome,and is effective on significantly improving lipid profiles and clinical symptoms.The combination therapy demonstrates superior efficacy compared to Atorvastatin Calcium Tablets alone.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.

Objective:To systematically evaluate the application of artificial intelligence (AI) in cervical cytopathology diagnosis.Methods:A systematic search was conducted using the keywords ′′cervical cancer′′ ′′cytology′′ ′′artificial intelligence′′ ′′sensitivity′′ and ′′specificity′′ (in both English and Chinese) across databases including PubMed, Web of Science, Embase, Cochrane Library, IEEE Xplore, CNKI, Wanfang, VIP Chinese Science and Technology Journals, and SinoMed. The search covered literature from inception until January 1, 2024, on the application of AI in cervical cytopathological diagnosis. Data were extracted using a predefined data extraction form to compile the contingency table data, from which sensitivity, specificity and area under the curve (AUC) were calculated.Results:A total of 1 616 articles were initially retrieved, and 27 articles were finally included in this study according to the inclusion and exclusion criteria. Five researches were conducted on the diagnosis of cytopathology slides, with pooled AUC, sensitivity and specificity of 0.92 (95% CI: 0.89-0.94), 0.91 (95% CI: 0.77-0.97) and 0.84 (95% CI: 0.77-0.90), respectively. About 22 researches were conducted on the diagnosis of cytopathology images (individual cells or cell clusters), with pooled AUC, sensitivity and specificity of 1.00 (95% CI: 0.99-1.00), 0.98 (95% CI: 0.97-0.99) and 0.98 (95% CI: 0.97-0.99), respectively. Conclusion:The application of AI in the field of cervical cytopathology shows certain diagnostic performance and potential clinical application value.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.

Objective:To systematically evaluate the application of artificial intelligence (AI) in cervical cytopathology diagnosis.Methods:A systematic search was conducted using the keywords ′′cervical cancer′′ ′′cytology′′ ′′artificial intelligence′′ ′′sensitivity′′ and ′′specificity′′ (in both English and Chinese) across databases including PubMed, Web of Science, Embase, Cochrane Library, IEEE Xplore, CNKI, Wanfang, VIP Chinese Science and Technology Journals, and SinoMed. The search covered literature from inception until January 1, 2024, on the application of AI in cervical cytopathological diagnosis. Data were extracted using a predefined data extraction form to compile the contingency table data, from which sensitivity, specificity and area under the curve (AUC) were calculated.Results:A total of 1 616 articles were initially retrieved, and 27 articles were finally included in this study according to the inclusion and exclusion criteria. Five researches were conducted on the diagnosis of cytopathology slides, with pooled AUC, sensitivity and specificity of 0.92 (95% CI: 0.89-0.94), 0.91 (95% CI: 0.77-0.97) and 0.84 (95% CI: 0.77-0.90), respectively. About 22 researches were conducted on the diagnosis of cytopathology images (individual cells or cell clusters), with pooled AUC, sensitivity and specificity of 1.00 (95% CI: 0.99-1.00), 0.98 (95% CI: 0.97-0.99) and 0.98 (95% CI: 0.97-0.99), respectively. Conclusion:The application of AI in the field of cervical cytopathology shows certain diagnostic performance and potential clinical application value.

Objective To analyze the relationship between the body mass index(BMI)and anal fistula occurrence based on the results of cross-sectional survey.Methods The clinical data of 545 patients with a-nal fistula visiting in the proctology department of this hospital from March 2022 to November 2023 were ana-lyzed.The patients were divided into 4 groups based on BMI:skinny group(BMI＜18.5 kg/m2),normal group(BMI 18.5-＜24.0 kg/m2),overweight group(BMI 24.0-＜28.0 kg/m2)and obese group(BMI ≥28.0 kg/m2).The logistic regression was used to analyze the influencing factors of fistula occurrence.The dose-response relationship between BMI and the occurrence risk of anal fistula was analyzed by combining with the restricted cubic spline(RCS)model.Results The logistic regression analysis results showed that obese was the risk factor of anal fistula occurrence(OR=2.08,95％CI:1.08-3.99,P=0.028).The combining RCS a-nalysis results showed that there was a"J"shaped trend between the BMI and the risk of anal fistula.In addi-tion,smoking could increase the risk of anal fistula occurrence in obese population.Conclusion Overweight and obesity are the risk factors for anal fistula occurrence.It is suggested that the susceptible population should control their body weight reasonably to prevent the occurrence of anal fistula.