Background The gold standard for the diagnosis of central precocious puberty (CPP) is gonadotropin-releasing hormone (GnRH) or GnRH analogs (GnRHa) stimulation test. But the stimulation test is time-consuming and costly. Our objective was to develop a risk score model readily adoptable by clinicians and patients. Methods A cross-sectional study based on the electronic medical record system was conducted in the Children’s Hospital, Fudan University, Shanghai, China from January 2010 to August 2016. Patients with precocious puberty were randomly split into the training (n = 314) and validation (n = 313) sample. In the training sample, variables associated with CPP (P < 0.2) in univariate analyses were introduced in a multivariable logistic regression model. Prediction model was selected using a forward stepwise analysis. A risk score model was built with the scaled coefficients of the model and tested in the validation sample. Results CPP was diagnosed in 54.8% (172/314) and 55.0% (172/313) of patients in the training and validation sample, respectively. The CPP risk score model included age at the onset of puberty, basal luteinizing hormone (LH) concentration, largest ovarian volume, and uterine volume. The C-index was 0.85 (95% CI: 0.81–0.89) and 0.86 (95% CI: 0.82–0.90) in the training and the validation sample, respectively. Two cut-off points were selected to delimitate a low- (< 10 points), median- (10–19 points), and high-risk (≥ 20 points) group. Conclusions A risk score model for the risk of CPP had a moderate predictive performance, which offers the advantage of helping evaluate the requirement for further diagnostic tests (GnRH or GnRHa stimulation test).

Introduction Erythropoiesis slowly decreases with increasing age, which may be reflected in red blood cell (RBC) parameters. This multicentre collaborative study aimed to investigate the changes in erythropoiesis with increasing age in a healthy Chinese population. Methods A total of 14,591 healthy individuals (6,713 aged at least 60 y and 7,878 aged below 60 y) from seven cities across China were enrolled. K2‐EDTA anticoagulant blood samples were analysed. The results are presented as median and 2.5‐97.5th percentile. Results RBC parameters showed some differences between the two groups divided by the age of 60 in the Chinese population. The median, 2.5th and 97.5th percentile values of RBC, haemoglobin (HGB) and haematocrit (HCT) in patients aged ≥ 60 y were significantly lower than in those ˂ 60 y. The values of mean cell volume (MCV), mean cell haemoglobin (MCH) and red cell distribution width (RDW) were higher in the group aged ≥ 60 y. Men had significantly higher RBC, HGB, HCT, MCV, MCH and RDW indices than women. The prevalence of anaemia gradually increased with age in men and was higher than that in women after 50. The median haemoglobin and MCV in Nanning and Guangzhou were lower than those in other regions. Conclusion RBC parameters varied with increasing age and differed between males and females, indicating that erythropoiesis decreases in the elderly Chinese population. Subsequent studies should be conducted for age‐ and sex‐specific reference intervals in healthy elderly Chinese populations.

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Background The gold standard for the diagnosis of central precocious puberty (CPP) is gonadotropin-releasing hormone (GnRH) or GnRH analogs (GnRHa) stimulation test. But the stimulation test is time-consuming and costly. Our objective was to develop a risk score model with readily available features.Methods A cross sectional study based on the electronic medical record system including 627 girls with precocious puberty were conducted in the Children’s Hospital, Fudan University, Shanghai, China from January 2010 to August 2016. Patients were randomly split into the training (n=314) and validation (n=313) sample. In the training sample, variables associated with CPP (P<0.2) in univariate analyses were introduced in a multivariable logistic regression model and selected using a forward stepwise analysis. A risk score model was built with the scaled coefficients of the model and tested in the validation sample.Results CPP was diagnosed in 54.8% (172/314) and 55.0% (172/313) of patients in the training and validation sample respectively. The CPP risk score model included variables of age at onset of puberty, basal luteinizing hormone (LH) concentration, largest ovarian volume, and uterine volume. The C-index was 0.85 (95% CI: 0.81-0.89) for the training sample and 0.86 (95% CI: 0.82-0.90) for the validation sample. Two cut-off points were selected to delimitate a low- (<10 points), median- (10-19 points), and high-risk (≥ 20 points) group. Conclusions A risk score model developed among girls with precocious pubertal development had moderate discrimination to stratify CPP risk, which could help make decisions on the need for GnRH (GnRHa) stimulation test.

Objectives: To establish and validate a linear model utilizing diaphragm motion (DM) to predict the displacement of liver tumors (DLTs) for patients who underwent carbon ion radiotherapy (CIRT). A total of 60 pairs of planning and reviewing four-dimensional computed tomography (4DCT) sets over 23 patients were used. Method: We constructed an averaged computed tomography (CT) set for each either planning or reviewing 4DCT within respiratory phases between 20% of exhale and inhale. A rigid image registration to align bony structures was performed between planning and reviewing 4DCT. The position changes on top of diaphragm in superior–inferior (SI) direction between 2 CTs to present DM were obtained. The translational vectors in SI from matching to present DLT were obtained. The linear model was built by training data for 23 imaging pairs. A distance model utilized the cumulative probability distribution (CPD) of DM or DLT and was compared with the linear model. We conducted the statistical regression analysis with receiver operating characteristic (ROC) testing data of 37 imaging pairs to validate the performance of our linear model. Results: The DM within 0.5 mm was true positive (TP) with an area under the ROC curve (AUC) of 0.983 to predict DLT. The error of predicted DLT within half of its mean value indicated the reliability of prediction method. The 23 pairs of data showed (4.5 ± 3.3) mm for trend of DM and (2.2 ± 1.6) mm for DLT. A linear model of DLT = 0.46*DM + 0.12 was established. The predicted DLT was (2.2 ± 1.5) mm with a prediction error of (0.3 ± 0.3) mm. The accumulated probability of observed and predicted DLT with < 5.0 mm magnitude was 93.2% and 94.5%, respectively. Conclusion: We utilized the linear model to set the proper beam gating for predicting DLT within 5.0 mm to treat patients. We will investigate a proper process on x-ray fluoroscopy images to establish a reliable model predicting DLT for DM observed in x-ray fluoroscopy in the following two years.