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Background. Lung metastasis greatly affects medical therapeutic strategies in osteosarcoma. This study aimed to develop and validate a clinical prediction model to predict the risk of lung metastasis among osteosarcoma patients based on machine learning (ML) algorithms. Methods. We retrospectively collected osteosarcoma patients from the Surveillance Epidemiology and End Results (SEER) database and from four hospitals in China. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and multilayer perceptron (MLP), were applied to build predictive models for predicting lung metastasis using patient's demographics, clinical characteristics, and therapeutic variables from the SEER database. The model was internally validated using 10-fold cross-validation to calculate the mean area under the curve (AUC) and the model was externally validated using the Chinese multicenter osteosarcoma data. Relative importance ranking of predictors was plotted to understand the importance of each predictor in different ML algorithms. The correlation heat map of predictors was plotted to understand the correlation of each predictor, selecting the 10-fold cross-validation with the highest AUC value in the external validation ROC curve to build a web calculator. Results. Of all enrolled patients from the SEER database, 17.73% (194/1094) developed lung metastasis. The multiple logistic regression analysis showed that sex, N stage, T stage, surgery, and bone metastasis were all independent risk factors for lung metastasis. In predicting lung metastasis, the mean AUCs of the six ML algorithms ranged from 0.711 to 0.738 in internal validation and 0.697 to 0.729 in external validation. Among the six ML algorithms, the extreme gradient boosting (XGBoost) model had the highest AUC value with an average internal AUC of 0.738 and an external AUC of 0.729. The best performing ML algorithm model was used to build a web calculator to facilitate clinicians to calculate the risk of lung metastasis for each patient. Conclusions. The XGBoost model may have the best prediction effect and the online calculator based on this model can help doctors to determine the lung metastasis risk of osteosarcoma patients and help to make individualized medical strategies. [ABSTRACT FROM AUTHOR]

Background: Regional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms. Methods: A total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals. Results: Of all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals. Conclusions: T and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients. [ABSTRACT FROM AUTHOR]

Background: The implementation of family doctor contract service is a pivotal measure to enhance primary medical services and execute the hierarchical diagnosis and treatment system. Achieving service coordination among various institutions is both a fundamental objective and a central element of contract services., Objective: The study aims to assess residents' evaluations and determining factors related to the coordination of health services within primary medical institutions across different regions of Shandong Province. The findings intend to serve as a reference for enhancing the coordination services offered by these institutions., Methods: The study employed a multi-stage stratified random sampling method to select three prefecture-level cities in Shandong Province with different economic levels. Within each city, three counties (districts) were randomly sampled using the same method. Within each county (district), three community health service centers and township health centers implementing family doctor contract services were selected randomly. Face-to-face questionnaire surveys were conducted with contracted residents using the coordination dimension of the revised Primary Care Assessment Tools Scale (PCAT) developed by the research team. Data analysis was conducted using such methods as one-way analysis of variance and multiple linear regression., Results: The sample included 3,859 contracted residents. The coordination dimension score of primary medical institutions averaged 3.41 ± 0.18, with the referral service sub-dimension scoring 3.60 ± 0.58 and the information system sub-dimension scoring 3.34 ± 0.65. The overall score of the referral service sub-dimension surpassed that of the information system sub-dimension. Regression results indicated that the city's economic status, the type of contracted institutions, gender, education, marital status, income, occupation, health status, and endowment insurance payment status significantly influenced the coordinated service score of primary medical institutions ( p  < 0.05)., Conclusion: The coordination of primary medical institutions in Shandong Province warrants further optimization. Continued efforts should focus on refining the referral system, expediting information infrastructure development, enhancing the service standards of primary medical institutions, and fostering resident trust. These measures aim to advance the implementation of the hierarchical diagnosis and treatment and two-way referral system., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xu, Xu, Jiang, Peng, Wu, Lang, Zhou, Ma, Chen, Yin and Yu.)