Preterm birth among pregnancies conceived by assisted reproduction techniques in Mumbai, Maharashtra, India

Background: Preterm births are an enormous global problem on families, medical system and economy. The rates of preterm birth are increasing and one of the contributors is growing use of Assisted Reproduction Techniques (ART) leading to multifetal gestations. Some risk factors for preterm birth are specific to women who conceive by ART. Since there is limited data from India, this pilot study was undertaken to assess the magnitude of preterm birth among pregnancies conceived by ART and to study the contributing factors.Methods: Clinic based descriptive cohort study through eight ART clinics in Mumbai for one year. Data was collected using an in-depth questionnaire on socio-demographic characteristics, medical history, ART details, course and complications during pregnancy, mode of delivery, pregnancy outcome, risk factors related to preterm birth and neonatal outcome. Complete details of 113 participants who completed the study were analyzed.Results: Study showed high incidence of preterm birth (76.23%) among women conceived with ART. Multiple gestations were observed in 45.1%. Pregnancy related complications like heterotrophic pregnancy (3%), pre eclampsia (15%) and gestational diabetes (11%) were high. Incidence of caesarean section was very high (98%). Neonatal outcome was good with 98% live births and only 2 still births.Conclusions: Present study highlights that preterm birth, multiple pregnancies, pregnancy related complications like preeclampsia, gestational diabetes and caesarean sections are very high among women conceived by ART. With growing use of ART there is an urgent need to develop a National ART Surveillance system in India like the one in Centre for Disease Control Atlanta to get complete data on the pregnancy course and outcomes of ART conceptions. Efforts to limit the number of embryos transferred should be strengthened to prevent multiple births

Preterm birth among pregnancies conceived by assisted reproduction techniques in Mumbai, Maharashtra, India

Background: Preterm births are an enormous global problem on families, medical system and economy. The rates of preterm birth are increasing and one of the contributors is growing use of Assisted Reproduction Techniques (ART) leading to multifetal gestations. Some risk factors for preterm birth are specific to women who conceive by ART. Since there is limited data from India, this pilot study was undertaken to assess the magnitude of preterm birth among pregnancies conceived by ART and to study the contributing factors.Methods: Clinic based descriptive cohort study through eight ART clinics in Mumbai for one year. Data was collected using an in-depth questionnaire on socio-demographic characteristics, medical history, ART details, course and complications during pregnancy, mode of delivery, pregnancy outcome, risk factors related to preterm birth and neonatal outcome. Complete details of 113 participants who completed the study were analyzed.Results: Study showed high incidence of preterm birth (76.23%) among women conceived with ART. Multiple gestations were observed in 45.1%. Pregnancy related complications like heterotrophic pregnancy (3%), pre eclampsia (15%) and gestational diabetes (11%) were high. Incidence of caesarean section was very high (98%). Neonatal outcome was good with 98% live births and only 2 still births.Conclusions: Present study highlights that preterm birth, multiple pregnancies, pregnancy related complications like preeclampsia, gestational diabetes and caesarean sections are very high among women conceived by ART. With growing use of ART there is an urgent need to develop a National ART Surveillance system in India like the one in Centre for Disease Control Atlanta to get complete data on the pregnancy course and outcomes of ART conceptions. Efforts to limit the number of embryos transferred should be strengthened to prevent multiple births

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