Background: Small for gestational age (SGA) is a condition in which fetal birthweight is below the 10th percentile for the gestational age, which increases the risk of perinatal morbidity and mortality. Therefore, early screening for each pregnant woman is of great interest. We aimed to develop an accurate and widely applicable screening model for SGA at 21–24 gestational weeks of singleton pregnancies. Methods: This retrospective observational study included medical records of 23,783 pregnant women who gave birth to singleton infants at a tertiary hospital in Shanghai between 1 January 2018 and 31 December 2019. The obtained data were nonrandomly classified into training (1 January 2018 to 31 December 2018) and validation (1 January 2019 to 31 December 2019) datasets based on the year of data collection. The study variables, including maternal characteristics, laboratory test results, and sonographic parameters at 21–24 weeks of gestation were compared between the two groups. Further, univariate and multivariate logistic regression analyses were performed to identify independent risk factors for SGA. The reduced model was presented as a nomogram. The performance of the nomogram was assessed in terms of its discrimination, calibration, and clinical usefulness. Moreover, its performance was assessed in the preterm subgroup of SGA. Results: Overall, 11,746 and 12,037 cases were included in the training and validation datasets, respectively. The developed SGA nomogram, comprising 12 selected variables, including age, gravidity, parity, body mass index, gestational age, single umbilical artery, abdominal circumference, humerus length, abdominal anteroposterior trunk diameter, umbilical artery systolic/diastolic ratio, transverse trunk diameter, and fasting plasma glucose, was significantly associated with SGA. The area under the curve value of our SGA nomogram model was 0.7, indicating a good identification ability and favorable calibration. Regarding preterm SGA fetuses, the nomogram achieved a satisfactory performance, with an average prediction rate of 86.3%. Conclusions: Our model is a reliable screening tool for SGA at 21–24 gestational weeks, especially for high-risk preterm fetuses. We believe that it will help clinical healthcare staff to arrange more comprehensive prenatal care examinations and, consequently, provide a timely diagnosis, intervention, and delivery.

Background Considering the high incidence of medical privacy disclosure, it is of vital importance to study doctors’ privacy protection behavior and its influencing factors. Objective We aim to develop a scale for doctors’ protection of patients’ privacy in Chinese public medical institutions, following construction of a theoretical model framework through grounded theory, and subsequently to validate the scale to measure this protection behavior. Methods Combined with the theoretical paradigm of protection motivation theory (PMT) and semistructured interview data, the grounded theory research method, followed by the Delphi expert and group discussion methods, a theoretical framework and initial scale for doctors in Chinese public medical institutions to protect patients' privacy was formed. The adjusted scale was collected online using a WeChat electronic survey measured using a 5-point Likert scale. Exploratory and confirmatory factor analysis (EFA and CFA) and tests to analyze reliability and validity were performed on the sample data. SPSS 19.0 and Amos 26.0 statistical analysis software were used for EFA and CFA of the sample data, respectively. Results According to the internal logic of PMT, we developed a novel theoretical framework of a “storyline,” which was a process from being unaware of patients' privacy to having privacy protection behavior, that affected doctors' cognitive intermediary and changed the development of doctors' awareness, finally affecting actual privacy protection behavior in Chinese public medical institutions. Ultimately, we created a scale to measure 18 variables in the theoretical model, comprising 63 measurement items, with a total of 208 doctors participating in the scaling survey, who were predominantly educated to the master’s degree level (n=151, 72.6%). The department distribution was relatively balanced. Prior to EFA, the Kaiser-Meyer-Olkin (KMO) value was 0.702, indicating that the study was suitable for factor analysis. The minimum value of Cronbach α for each study variable was .754, which met the internal consistency requirements of the scale. The standard factor loading value of each potential measurement item in CFA had scores greater than 0.5, which signified that all the items in the scale could effectively converge to the corresponding potential variables. Conclusions The theoretical framework and scale to assess doctors' patient protection behavior in public medical institutions in China fills a significant gap in the literature and can be used to further the current knowledge of physicians’ thought processes and adoption decisions.

BACKGROUND Considering the high incidence of medical privacy disclosure, it is of vital importance to study doctors’ privacy protection behavior and its influencing factors. OBJECTIVE We aimed to explore the internal mechanism of doctors' protection of patients' privacy in Chinese public medical institutions using of grounded theory, in order to construct a theoretical model framework, to develop, and subsequently to validate a scale to measure this protection behavior. METHODS Combined with PMT and interview data, the grounded theory research method, and followed by the Delphi expert and group discussion methods, a theoretical framework and initial scale for doctors in Chinese public medical institutions to protect patients' privacy was formed. The adjusted scale was collected online using a WeChat electronic survey. Exploratory and confirmatory factor analyzes and tests to analyze reliability and validity were performed on the sample data. RESULTS This study formed a theory regarding doctors' privacy protection behavior of patients in Chinese public medical institutions. Ultimately, we created a scale to measure 18 variables in the theoretical model, comprised of 63 measurement items, with a total of 208 doctors participating in the scaling survey. The research demonstrated that the scale had respectable internal consistency, in which the fit of the confirmatory factor model was good. CONCLUSIONS The theoretical framework and scale to assess doctors' patient protection behavior in public medical institutions in China fills a significant gap in the literature and can be used to further the current knowledge of physicians’ thought processes and adoption decisions.

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Aim: To develop a nomogram model for the prediction of macrosomia using real-world clinical data.Methods: In the present study, we retrospectively analyzed the medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We extracted the data from a total of 13,403 pregnant women for this study, with the original dataset split into a training set (n = 9,382) and a validation set (n = 4,021) at a 7:3 ratio to generate and validate our model. The independent risk factors for macrosomia in pregnant women were analyzed employing multivariate logistic regression, and the nomogram model used to predict the risk of macrosomia in pregnant women was established and verified with R software.Results: We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model.Conclusions: Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.