Missed injuries in trauma patients: A literature review

<p>Abstract</p> <p>Background</p> <p>Overlooked injuries and delayed diagnoses are still common problems in the treatment of polytrauma patients. Therefore, ongoing documentation describing the incidence rates of missed injuries, clinically significant missed injuries, contributing factors and outcome is necessary to improve the quality of trauma care. This review summarizes the available literature on missed injuries, focusing on overlooked muscoloskeletal injuries.</p> <p>Methods</p> <p>Manuscripts dealing with missed injuries after trauma were reviewed. The following search modules were selected in PubMed: Missed injuries, Delayed diagnoses, Trauma, Musculoskeletal injuires. Three time periods were differentiated: (n = 2, 1980–1990), (n = 6, 1990–2000), and (n = 9, 2000-Present).</p> <p>Results</p> <p>We found a wide spread distribution of missed injuries and delayed diagnoses incidence rates (1.3% to 39%). Approximately 15 to 22.3% of patients with missed injuries had clinically significant missed injuries. Furthermore, we observed a decrease of missed pelvic and hip injuries within the last decade.</p> <p>Conclusion</p> <p>The lack of standardized studies using comparable definitions for missed injuries and clinically significant missed injuries call for further investigations, which are necessary to produce more reliable data. Furthermore, improvements in diagnostic techniques (e.g. the use of multi-slice CT) may lead to a decreased incidence of missed pelvic injuries. Finally, the standardized tertiary trauma survey is vitally important in the detection of clinically significant missed injuries and should be included in trauma care.</p

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community

Veto Players in Post-Conflict DDR Programs: Evidence from Nepal and the DRC

Under what conditions are Disarmament, Demobilization and Reintegration (DDR) programs successfully implemented following intrastate conflict? Previous research is dominated by under-theorized case studies that lack the ability to detect the precise factors and mechanisms that lead to successful DDR. In this article, we draw on game theory and ask how the number of veto players, their policy distance, and their internal cohesion impact DDR implementation. Using empirical evidence from Nepal and the Democratic Republic of Congo, we show that the number of veto players, rather than their distance and cohesion, explains the (lack of) implementation of DDR