Cranial and ventricular size following shunting or endoscopic third ventriculostomy (ETV) in infants with aqueductal stenosis: further insights from the International Infant Hydrocephalus Study (IIHS)

Purpose: The craniometrics of head circumference (HC) and ventricular size are part of the clinical assessment of infants with hydrocephalus and are often utilized in conjunction with other clinical and radiological parameters to determine the success of treatment. We aimed to assess the effect of endoscopic third ventriculostomy (ETV) and shunting on craniometric measurements during the follow-up of a cohort of infants with symptomatic triventricular hydrocephalus secondary to aqueductal stenosis. Methods: We performed a post hoc analysis of data from the International Infant Hydrocephalus Study (IIHS)—a prospective, multicenter study of infants (\u3c 24 months old) with hydrocephalus from aqueductal stenosis who were treated with either an ETV or shunt. During various stages of a 5-year follow-up period, the following craniometrics were measured: HC, HC centile, HC z-score, and frontal-occipital horn ratio (FOR). Data were compared in an analysis of covariance, adjusting for baseline variables including age at surgery and sex. Results: Of 158 enrolled patients, 115 underwent an ETV, while 43 received a shunt. Both procedures led to improvements in the mean HC centile position and z-score, a trend which continued until the 5-year assessment point. A similar trend was noted for FOR which was measured at 12 months and 3 years following initial treatment. Although the values were consistently higher for ETV compared with shunt, the differences in HC value, centile, and z-score were not significant. ETV was associated with a significantly higher FOR compared with shunting at 12 months (0.52 vs 0.44; p = 0.002) and 3 years (0.46 vs 0.38; p = 0.03) of follow-up. Conclusion: ETV and shunting led to improvements in HC centile, z-score, and FOR measurements during long-term follow-up of infants with hydrocephalus secondary to aqueductal stenosis. Head size did not significantly differ between the treatment groups during follow-up, however ventricle size was greater in those undergoing ETV when measured at 1 and 3 years following treatment

Cranial and ventricular size following shunting or endoscopic third ventriculostomy (ETV) in infants with aqueductal stenosis: further insights from the International Infant Hydrocephalus Study (IIHS)

Purpose The craniometrics of head circumference (HC) and ventricular size are part of the clinical assessment of infants with hydrocephalus and are often utilized in conjunction with other clinical and radiological parameters to determine the success of treatment. We aimed to assess the effect of endoscopic third ventriculostomy (ETV) and shunting on craniometric measurements during the follow-up of a cohort of infants with symptomatic triventricular hydrocephalus secondary to aqueductal stenosis. Methods We performed a post hoc analysis of data from the International Infant Hydrocephalus Study (IIHS)-a prospective, multicenter study of infants (< 24 months old) with hydrocephalus from aqueductal stenosis who were treated with either an ETV or shunt. During various stages of a 5-year follow-up period, the following craniometrics were measured: HC, HC centile, HCz-score, and frontal-occipital horn ratio (FOR). Data were compared in an analysis of covariance, adjusting for baseline variables including age at surgery and sex. Results Of 158 enrolled patients, 115 underwent an ETV, while 43 received a shunt. Both procedures led to improvements in the mean HC centile position andz-score, a trend which continued until the 5-year assessment point. A similar trend was noted for FOR which was measured at 12 months and 3 years following initial treatment. Although the values were consistently higher for ETV compared with shunt, the differences in HC value, centile, andz-score were not significant. ETV was associated with a significantly higher FOR compared with shunting at 12 months (0.52 vs 0.44;p = 0.002) and 3 years (0.46 vs 0.38;p = 0.03) of follow-up. Conclusion ETV and shunting led to improvements in HC centile,z-score, and FOR measurements during long-term follow-up of infants with hydrocephalus secondary to aqueductal stenosis. Head size did not significantly differ between the treatment groups during follow-up, however ventricle size was greater in those undergoing ETV when measured at 1 and 3 years following treatment

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