Emergency general surgery in Rwandan district hospitals: a cross-sectional study of spectrum, management, and patient outcomes

Background: Management of emergency general surgical conditions remains a challenge in rural sub-Saharan Africa due to issues such as insufficient human capacity and infrastructure. This study describes the burden of emergency general surgical conditions and the ability to provide care for these conditions at three rural district hospitals in Rwanda. Methods: This retrospective cross-sectional study included all patients presenting to Butaro, Kirehe and Rwinkwavu District Hospitals between January 1st 2015 and December 31st 2015 with emergency general surgical conditions, defined as non-traumatic, non-obstetric acute care surgical conditions. We describe patient demographics, clinical characteristics, management and outcomes. Results: In 2015, 356 patients presented with emergency general surgical conditions. The majority were male (57.2%) and adults aged 15–60 years (54.5%). The most common diagnostic group was soft tissue infections (71.6%), followed by acute abdominal conditions (14.3%). The median length of symptoms prior to diagnosis differed significantly by diagnosis type (p < 0.001), with the shortest being urological emergencies at 1.5 days (interquartile range (IQR):1, 6) and the longest being complicated hernia at 17.5 days (IQR: 1, 208). Of all patients, 54% were operated on at the district hospital, either by a general surgeon or general practitioner. Patients were more likely to receive surgery if they presented to a hospital with a general surgeon compared to a hospital with only general practitioners (75% vs 43%, p < 0.001). In addition, the general surgeon was more likely to treat patients with complex diagnoses such as acute abdominal conditions (33.3% vs 4.1%, p < 0.001) compared to general practitioners. For patients who received surgery, 73.3% had no postoperative complications and 3.2% died. Conclusion: While acute abdominal conditions are often considered the most common emergency general surgical condition in sub-Saharan Africa, soft tissue infections were the most common in our setting. This could represent a true difference in epidemiology in rural settings compared to referral facilities in urban settings. Patients were more likely to receive an operation in a hospital with a general surgeon as opposed to a general practitioner. This provides evidence to support increasing the surgical workforce in district hospitals in order to increase surgical availability for patients

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