ObjectiveTo develop and validate a diagnostic prediction model for differentiating pulmonary tuberculosis from community-acquired pneumonia based on routine blood parameters. MethodsA total of 642 patients with pulmonary tuberculosis and 503 patients with community-acquired pneumonia were retrospectively enrolled from the Seventh Hospital of Tangshan. They were randomly divided into a training set and an internal validation set at a 7∶3 ratio. Additionally， 218 patients from the 981st Hospital were independently included as an external validation set. The Boruta algorithm and recursive feature elimination were employed to select predictors from 82 blood parameters， sex， and age. A multivariate Logistic regression model was established and evaluated using receiver operating characteristic （ROC） curves， calibration curves， and decision curve analysis. ResultsThe final model incorporated eight predictors， namely uric acid， urea nitrogen， alkaline phosphatase， monoamine oxidase， alanine aminotransferase， glutathione， neutrophil percentage， and age. The model achieved areas under the curve （AUCs） of 0.800 （95%CI： 0.769-0.830）， 0.787 （95%CI： 0.738-0.836）， and 0.736 （95%CI： 0.667-0.835） in the training， internal validation， and external validation sets， respectively. The model demonstrated good calibration， and decision curve analysis showed clinical net benefit within the threshold probability range of 10%-80%. ConclusionThe developed model exhibits good discriminative ability and clinical utility， serving as an effective early screening tool for primary healthcare institutions.

Objective With the complex evolution of the social system,patients experience varying degrees of delay in obtaining medical and health services under the combined drive of internal and external environment.In order to alleviate the dilemma of seeking medical treatment in crisis situations,it analyzes the causes of the public's use behavior of Internet medical and health services after delayed medical treatment and the ways to improve it.Methods It employs the method of QCA analysis to explore the crisis management strategies adopted by 183 public.Based on Anderson medical treatment model as the theoretical guidance,conditional variables were constructed.Through deductive logic,it analyzes the complex linkage effect of seven variables,including accessibility level,traumatic life events,panic mood and satisfaction with Internet medical use,on the use of Internet medical services.Results it found that panic has a decisive role in promoting the public's use of Internet medical services.Five behavioural models of Internet medical use are proposed to reveal the potential driving mechanism of the public's adoption of Internet medical strategies.Conclusion By analyzing the synergistic effects between variables,it proposes comprehensive strategies,providing a new perspective for public health emergency management,and offering empirical evidence for policymakers to optimize smart medical services.

Objective With the complex evolution of the social system,patients experience varying degrees of delay in obtaining medical and health services under the combined drive of internal and external environment.In order to alleviate the dilemma of seeking medical treatment in crisis situations,it analyzes the causes of the public's use behavior of Internet medical and health services after delayed medical treatment and the ways to improve it.Methods It employs the method of QCA analysis to explore the crisis management strategies adopted by 183 public.Based on Anderson medical treatment model as the theoretical guidance,conditional variables were constructed.Through deductive logic,it analyzes the complex linkage effect of seven variables,including accessibility level,traumatic life events,panic mood and satisfaction with Internet medical use,on the use of Internet medical services.Results it found that panic has a decisive role in promoting the public's use of Internet medical services.Five behavioural models of Internet medical use are proposed to reveal the potential driving mechanism of the public's adoption of Internet medical strategies.Conclusion By analyzing the synergistic effects between variables,it proposes comprehensive strategies,providing a new perspective for public health emergency management,and offering empirical evidence for policymakers to optimize smart medical services.