A Survey of Perinatologists: Amniotic Fluid Index or Deepest Vertical Pocket?

INTRODUCTION: A 2009 Cochrane review found that amniotic fluid index use increases the rates of oligohydramnios and labor induction in singletons without improvement of perinatal outcomes when compared with the use of the deepest vertical pocket. We sought to determine the use of either amniotic fluid index or deepest vertical pocket among Society of Maternal-Fetal Medicine members. METHODS: Registered Society of Maternal-Fetal Medicine members were contacted by mail from September 2012 and February 2013 and asked to participate in a web-based survey addressing the use of amniotic fluid index and deepest vertical pocket. RESULTS: Two hundred twelve members participated (9.9%). Deepest vertical pocket was considered the most accurate method of evaluating amniotic fluid in the second trimester regardless of years since fellowship (10 years or less 61.8% compared with 10 years or greater 68.9%, P=.18) or practice type (academic 35.5% compared with nonacademic 47.1%, P=.36). Amniotic fluid index was considered the most accurate method of evaluating fluid in the third trimester regardless of years since fellowship (10 years or less 60.3% compared with greater than 10 years 53.9%, P=.59) or practice type (academic 62.7% compared with nonacademic 73.9%, P=.50). Most respondents thought antepartum interventions were more common when fluid is documented as low by amniotic fluid index (). One hundred eleven respondents (52.3%) replied oligohydramnios is overdiagnosed when using amniotic fluid index compared with deepest vertical pocket. Of 72 using amniotic fluid index, 50% replied they were unsure the Cochrane review merited a practice change, 41.7% replied that it is hard to change from using amniotic fluid index, and 30.6% thought more data favoring deepest vertical pocket are needed.(Figure is included in full-text article.) CONCLUSION: : Variations in evaluating amniotic fluid persist, suggesting the need for consensus in the diagnosis and management of low amniotic fluid in singleton gestations

Long-Term Survival of Hydrated Resting Eggs from Brachionus plicatilis

Several organisms display dormancy and developmental arrest at embryonic stages. Long-term survival in the dormant form is usually associated with desiccation, orthodox plant seeds and Artemia cysts being well documented examples. Several aquatic invertebrates display dormancy during embryonic development and survive for tens or even hundreds of years in a hydrated form, raising the question of whether survival in the non-desiccated form of embryonic development depends on pathways similar to those occurring in desiccation tolerant forms

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