Prenatal screening for preeclampsia: the roles of placental growth factor and pregnancy–associated plasma protein A in the first trimester and placental growth factor and soluble fms-like tyrosine kinase 1–placental growth factor ratio in the early second trimesterAJOG Global Reports at a Glance

BACKGROUND: Professional societies have recommended universal first trimester screening for preeclampsia and a second or third trimester soluble fms-like tyrosine kinase-1–placental growth factor ratio test to assess for preeclampsia and its severity. However, it may not be feasible to implement the most optimal screening protocol for preeclampsia in the first trimester which uses a combination of maternal characteristics, maternal biophysical and biochemical markers due to limitations in the access to uterine artery doppler ultrasound. There are inconsistent findings on how early in the second trimester the fms-like tyrosine kinase-1–placental growth factor ratio begins to provide useful information in preeclampsia prediction. OBJECTIVE: This study aimed to assess the accuracy of (1) a combination of maternal characteristics, maternal serum pregnancy-associated plasma protein A, and placental growth factor in the screening for preeclampsia in the first trimester; and (2) placental growth factor or soluble fms-like tyrosine kinase-1–placental growth factor ratio in the prediction of preeclampsia in the early second trimester. STUDY DESIGN: This retrospective case–control study used frozen residual blood samples from women who had aneuploidy screening and delivered at a tertiary center. The case group included pregnancies with gestational hypertension or preeclampsia (further classified as early-onset [birth at <34 weeks’ gestation] and preterm preeclampsia [birth at <37 weeks’ gestation]). Each case was matched with 3 control pregnancies by date of blood sample draw, gestational age at first blood sample draw, maternal age, maternal ethnicity, type of multiple-marker screening, and amount of residual sample. Mann–Whitney U tests were used to assess the associations between serum markers and the risk of preeclampsia. Logistic regressions were used to assess if the risk of preeclampsia can be predicted using a combination of maternal characteristics and serum markers. RESULTS: The case group included 146 preeclampsia and 295 gestational hypertension cases. Compared with the controls, preeclampsia cases had significantly lower first-trimester pregnancy-associated plasma protein A and placental growth factor. At a 20% false-positive rate, 71% of early-onset and 58% of preterm preeclampsia cases can be predicted using maternal characteristics, pregnancy-associated plasma protein A, and placental growth factor. Preeclampsia cases had lower second-trimester placental growth factor and a higher soluble fms-like tyrosine kinase-1–placental growth factor ratio. At a 10% false-positive rate, 80% and 53% of early-onset preeclampsia can be predicted using maternal characteristics and placental growth factor or soluble fms-like tyrosine kinase-1–placental growth factor ratio, respectively. CONCLUSION: The current first-trimester aneuploidy screening programs may be expanded to identify women at increased risk of developing preeclampsia. Early in the second trimester, placental growth factor alone provided better prediction for preeclampsia compared with the soluble fms-like tyrosine kinase-1–placental growth factor ratio

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc