Objective To verify the reliability of novel 3D classification of intertrochanteric fractures by comparing the consistency between conventional and novel classifications.Methods Included for the present study were the preoperative X-ray and CT images of 189 patients with intertrochanteric fracture who had been hospitalized at Department of Orthopaedics,Beijing Chao Yang Hospital,Capital Medical University from 1 January,2017 to 1 January,2019.The patients' intertrochanteric fractures were classified by 6 orthopedic surgeons independently using Evans classification,Jensen classification,AO classification and novel 3D classification,respectively.One month later,the original images of the 189 patients were renumbered and classified again in the same way.The Kappa values between observers and within observers were calculated for the classifications of intertrochanteric fractures based on X-ray and CT images.Results In Evans classification,Jensen classification,AO classification and novel 3D classification,the interobserver Kappa values of X-ray films were 0.54 ± 0.03,0.53 ± 0.03,0.45 ± 0.03 and 0.63 ± 0.02,respectively,and the interobserver Kappa values of the CT images were 0.49 ± 0.03,0.49 ± 0.03,0.44 ± 0.04 and 0.63 ± 0.03.The intraobserver Kappa values of the X-ray films were 0.53 ± 0.02,0.54 ± 0.03,0.44 ±0.04 and 0.65 ± 0.02,respectively,and the intraobserver Kappa values of the CT images were 0.52 ± 0.03,0.52 ±0.03,0.41 ±0.02 and 0.64 ±0.03.In the novel classification based on X-ray and CT images,the interobserver and intraobserver Kappa values were both significantly higher than those in Evans,Jensen and AO classifications (P ＜ 0.05).Conclusion The novel 3D classification of intertrochanteric fractures is more reliable than the conventional ones.

ObjectiveTo achieve high-dimensional prediction of class imbalanced of adverse drug reaction（ADR） of traditional Chinese medicine（TCM） and to classify and identify risk factors affecting the occurrence of ADR based on the post-marketing safety data of TCM monitored centrally in real world hospitals. MethodThe ensemble clustering resampling combined with regularized Group Lasso regression was used to perform high-dimensional balancing of ADR class-imbalanced data， and then to integrate the balanced datasets to achieve ADR prediction and the risk factor identification by category. ResultA practical example study of the proposed method on a monitoring data of TCM injection performed that the accuracy of the ADR prediction， the prediction sensitivity， the prediction specificity and the area under receiver operating characteristic curve（AUC） were all above 0.8 on the test set. Meanwhile， 40 risk factors affecting the occurrence of ADR were screened out from total 600 high-dimensional variables. And the effect of risk factors on the occurrence of ADR was identified by classification weighting. The important risk factors were classified as follows：past history， medication information， name of combined drugs， disease status， number of combined drugs and personal data. ConclusionIn the real world data of rare ADR with a large amount of clinical variables， this paper realized accurate ADR prediction on high-dimensional and class imbalanced condition， and classified and identified the key risk factors and their clinical significance of categories， so as to provide risk early warning for clinical rational drug use and combined drug use， as well as scientific basis for reevaluation of safety of post-marketing TCM.