A Controlled Clinical Trial of E5 Murine Monoclonal IgM Antibody to Endotoxin in the Treatment of Gram-Negative Sepsis

Objective. —To assess the efficacy of adjunctive monoclonal antibody antiendotoxin immunotherapy in patients with gram-negative sepsis. Design. —Double-blind, randomized, placebo-controlled trial. Setting. —Thirty-three university-affiliated centers, including Veterans Affairs, community, and municipal hospitals. Patients. —Hospitalized adults with signs of gram-negative infection and a systemic septic response. Intervention. —Patients were assigned to receive either 2 mg/kg of a murine monoclonal antibody directed against gram-negative endotoxin (E5) or placebo. A second infusion was administered 24 hours later. Main Outcome Measures. —Mortality over the 30-day study period, resolution of organ failures, and safety. Results. —Four hundred eighty-six patients were enrolled. Three hundred sixteen had confirmed gram-negative sepsis (54% bacteremic, 46% nonbacteremic). The survival difference was not statistically significant for all patients. Among patients with gram-negative sepsis who were not in shock at study entry (n = 137), E5 treatment resulted in significantly greater survival (relative risk, 2.3; P =.01). Resolution of individual organ failures was more frequent among these patients, occurring in 19 (54%) of 35 patients in the E5 group vs eight (30%) of 27 in the placebo group (P =.05). Four reversible allergic reactions occurred among 247 patients (1.6%) receiving E5. No other toxicity was identified. Conclusions. —Treatment with E5 antiendotoxin antibody appears safe. It reduces mortality and enhances the resolution of organ failure among patients with gram-negative sepsis who are not in shock when treated.(JAMA. 1991;266:1097-110

A Controlled Clinical Trial of E5 Murine Monoclonal IgM Antibody to Endotoxin in the Treatment of Gram-Negative Sepsis

Objective. —To assess the efficacy of adjunctive monoclonal antibody antiendotoxin immunotherapy in patients with gram-negative sepsis. Design. —Double-blind, randomized, placebo-controlled trial. Setting. —Thirty-three university-affiliated centers, including Veterans Affairs, community, and municipal hospitals. Patients. —Hospitalized adults with signs of gram-negative infection and a systemic septic response. Intervention. —Patients were assigned to receive either 2 mg/kg of a murine monoclonal antibody directed against gram-negative endotoxin (E5) or placebo. A second infusion was administered 24 hours later. Main Outcome Measures. —Mortality over the 30-day study period, resolution of organ failures, and safety. Results. —Four hundred eighty-six patients were enrolled. Three hundred sixteen had confirmed gram-negative sepsis (54% bacteremic, 46% nonbacteremic). The survival difference was not statistically significant for all patients. Among patients with gram-negative sepsis who were not in shock at study entry (n = 137), E5 treatment resulted in significantly greater survival (relative risk, 2.3; P =.01). Resolution of individual organ failures was more frequent among these patients, occurring in 19 (54%) of 35 patients in the E5 group vs eight (30%) of 27 in the placebo group (P =.05). Four reversible allergic reactions occurred among 247 patients (1.6%) receiving E5. No other toxicity was identified. Conclusions. —Treatment with E5 antiendotoxin antibody appears safe. It reduces mortality and enhances the resolution of organ failure among patients with gram-negative sepsis who are not in shock when treated.(JAMA. 1991;266:1097-110

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