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 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood develop- ment, 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

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Management of jaundice in the critically ill

There are many challenges that face intensivists identifying causes of jaundice in intensive care unit (ICU) patients. There is often an inevitable delay in correctly recognizing the aetiology, which can impact on initiating therapy. Despite appropriate institution of therapy in the form of fluids, vasoactive agents, mechanical ventilation, and nutrition, there is a possibility of inadvertently worsening hepatic injury due to various pathophysiological mechanisms discussed previously. This chapter aims to provide principles of management of the acutely-jaundiced ICU patient, focusing upon strategies to prevent hepatic injury, then reviewing therapeutic strategies for the management of liver injury per se. Specific treatment strategies are matched to aetiological categories. Definitive management should not delay initial resuscitation measures that can prevent secondary injury to vital organs. Some novel therapies are also discussed that might play a key role in future patient management.</p

Pathophysiology and causes of jaundice in the critically ill

Critically-ill patients develop jaundice for a variety of reasons. A good understanding of bilirubin metabolism can help the clinician to diagnose and treat jaundice. Intensive care unit (ICU) physicians commonly encounter elevated serum bilirubin in severely-ill patients, which can be associated with increased morbidity and mortality. A complex interaction of enzymatic pathways leads to safe excretion of bilirubin. This fine homeostasis is often disturbed and leads jaundice, which can be broadly classified into three main categories—prehepatic, hepatic, and post-hepatic. Common examples include sepsis, cardiac failure, drug toxicity, hepatic ischaemia, gall stone disease, etc. Management strategies directed towards the underlying causes aim to improve outcome. The aetiology can be often multifactorial and difficult to treat. This chapter provides a brief overview of bilirubin metabolism and aetiopathogenesis of jaundice. We also provide key recommendations to develop a systematic diagnostic approach, provide guidance on ordering appropriate investigations and on interpreting their results.</p

Evaluation of Noise Levels in Intensive Care Units in Two Large Teaching Hospitals – A Prospective Observational Study

Critically ill patients do not sleep well. One of the major environmental factors influencing sleep is noise. We prospectively measured noise levels and their relation to the time of day and location in different parts of two separate intensive care units (ICUs). Maximum, minimum and average noise levels were collected over 24 hour periods on five random days in both ICUs using digital sound meters, measured by the A-weighted decibel scale (dB (A)). The World Health Organisation (WHO) recommends that the average background noise in hospitals should not exceed 35 dB (A), and that peaks during the night should be less than 40 dB (A). The measured noise levels in both ICUs were well above the WHO standards. We recommend that various aspects, including staff education and modification of ICU design, must be carefully considered to provide an optimum environment for critically ill patients. </jats:p