Objective To investigate the effect of axial load test in Taylor spatial frame treatment of external fixation for tibia and fibula fractures.Methods A retrospective case-control study was conducted to analyze the clinical data of 36 patients with open fracture of tibia and fibula admitted to Tianjin Hospital from March 2015 to June 2017.There were 22 males and 14 females,aged 21-71 years[(46.1±14.2)years].All patients received Taylor spatial frame external fixation for tibia and fibula fracture within 1 week after injury.After operation,18 patients received axial load test(experiment group),and the other 18 did not(control group).When the value of axial load test was less than 5% in experiment group,the Taylor spatial frame was removed.The control group used traditional method to remove the Taylor spatial frame.Comparisons were made between the two groups in terms of treatment duration,total cost,re-fracture after Taylor spatial frame removal and incidence of stent-tract infection.Results All patients were followed up for 3-14 months with an average of 8.6 months.Compared with control group,the treatment duration[(36.17±11 .44)weeks vs.(44.50±9.16)weeks]and total cost[(93.7±7.9)thousand yuan vs.(120.1±10.6)thousand yuan]of experiment group were significantly lower(P＜0.05).In the experiment group,there was 0 patient with re-fracture and two patients with stent-tract infection,with the complication incidence of 11%,while there were two patients with re-fracture and three patients with stent-tract infection,with the complication incidence of 28% in the control group(P＞0.05).Conclusions After Taylor spatial frame external fixation for tibia and fibula fractures,regular axial load test can safely and timely guide the removal of Taylor spatial frame.It can reduce the treatment duration and cost compared with the traditional removal method,being safe and reliable.

OBJECTIVE: To compare the performance of machine learning models and traditional Cox regression model in predicting postoperative outcomes of patients with esophagogastric junction adenocarcinoma (AEG). METHODS: This study was conducted among 203 AEG patients with complete clinical and follow-up data, who were treated in our hospital between September, 2015 and October, 2020. The clinicopathological data of the patients were processed for analysis using R language package and divided into training and validation datasets at the ratio of 3:1. The Cox proportional hazards regression model and 4 machine learning models were constructed for analyzing the datasets. ROC curves, calibration curves and clinical decision curves (DCA) were plotted. Internal validation of the machine learning models was performed to assess their predictive efficacy. The predictive performance of each model was evaluated by calculating the area under the curve (AUC), and the model fitting was assessed using the calibration curve. RESULTS: For predicting 3-year survival based on the validation dataset, the AUC was 0.870 for Cox proportional hazard regression model, 0.901 for eXtreme Gradient Boosting (XGBoost), 0.791 for random forest, 0.832 for support vector machine, and 0.725 for multilayer perceptron; For predicting 5-year survival, the AUCs of these models were 0.915, 0.916, 0.758, 0.905, and 0.737, respectively. For internal validation, the AUCs of the 4 machine learning models decreased in the order of XGBoost (0.818), random forest (0.758), support vector machine (0.0.804), and multilayer perceptron (0.745). CONCLUSION The machine learning models show better predictive efficacy for survival outcomes of patients with AEG than Cox proportional hazard regression model, especially when proportional odds assumption or linear regression models are not applicable. XGBoost models have better performance than the other machine learning models, and the multi-layer perception model may have poor fitting results for a limited data volume.
Humans ; Adenocarcinoma ; Prognosis ; Machine Learning ; Esophagogastric Junction