Clinical Diagnosis of Placenta Accreta and Clinicopathological Outcomes

Objective To investigate the association between the intraoperative diagnosis of placenta accreta at the time of cesarean hysterectomy and pathological diagnosis. Study Design This is a retrospective cohort study of all patients undergoing cesarean hysterectomy for suspected placenta accreta from 2000 to 2016 at Barnes-Jewish Hospital. The primary outcome was the presence of invasive placentation on the pathology report. We estimated predictive characteristics of clinical diagnosis of placenta accreta using pathological diagnosis as the correct diagnosis. Results There were 50 cesarean hysterectomies performed for suspected abnormal placentation from 2000 to 2016. Of these, 34 (68%) had a diagnosis of accreta preoperatively and 16 (32%) were diagnosed intraoperatively at the time of cesarean delivery. Two patients had no pathological evidence of invasion, corresponding to a false-positive rate of 4% (95% confidence interval [CI]: 0.5%, 13.8%) and a positive predictive value of 96% (95% CI: 86.3%, 99.5%). There were no differences in complications among patients diagnosed intraoperatively compared with those diagnosed preoperatively. Conclusion Most patients undergoing cesarean hysterectomy for placenta accreta do have this diagnosis confirmed on pathology. However, since the diagnosis of placenta accreta was made intraoperatively in nearly a third of cesarean hysterectomies, intraoperative vigilance is required as the need for cesarean hysterectomy may not be anticipated preoperatively

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models

Insights into accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data