Prediction of poor outcome in clostridioides difficile infection: A multicentre external validation of the toxin B amplification cycle

Producción CientíficaClassification of patients according to their risk of poor outcomes in Clostridioides difficile infection (CDI) would enable implementation of costly new treatment options in a subset of patients at higher risk of poor outcome. In a previous study, we found that low toxin B amplification cycle thresholds (Ct) were independently associated with poor outcome CDI. Our objective was to perform a multicentre external validation of a PCR-toxin B Ct as a marker of poor outcome CDI. We carried out a multicentre study (14 hospitals) in which the characteristics and outcome of patients with CDI were evaluated. A subanalysis of the results of the amplification curve of real-time PCR gene toxin B (XpertTM C. difficile) was performed. A total of 223 patients were included. The median age was 73.0 years, 50.2% were female, and the median Charlson index was 3.0. The comparison of poor outcome and non–poor outcome CDI episodes revealed, respectively, the following results: median age (years), 77.0 vs 72.0 (p = 0.009); patients from nursing homes, 24.4% vs 10.8% (p = 0.039); median leukocytes (cells/μl), 10,740.0 vs 8795.0 (p = 0.026); and median PCR-toxin B Ct, 23.3 vs 25.4 (p = 0.004). Multivariate analysis showed that a PCR-toxin B Ct cut-off <23.5 was significantly and independently associated with poor outcome CDI (p = 0.002; OR, 3.371; 95%CI, 1.565–7.264). This variable correctly classified 68.5% of patients. The use of this microbiological marker could facilitate early selection of patients who are at higher risk of poor outcome and are more likely to benefit from newer and more costly therapeutic options

The Psychological Science Accelerator: Advancing Psychology Through a Distributed Collaborative Network

Source at https://doi.org/10.1177/2515245918797607.Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability

Impact of Lockdown on COVID-19 Transmissibility During the First Pandemic Wave in Spain

Background: The analysis of the evolution of the COVID-19 epidemic can provide evidence of the impact of measures implemented to reduce its progression. Our aim was to describe the evolution of the pandemic in the different Spanish regions and to examine the effect of the non-pharmaceutical public health interventions during the first epidemic wave on these trends. Methods: Daily incidence rates of cases were calculated at national and regional level between 31th of January and 10th of May 2020. Epidemic curves, important dates of interventions and effective reproduction number (Rt) were plotted and transmissibility parameters were calculated. To summarize the geographical heterogeneity in the evolution, regional epidemic curves have been classified into homogeneous groups using a clustering procedure. Findings: The incidence rate reached 5 cases per 100,000 on March 1 and peaked at March 20. The Rt gradually decreased after the national lockdown falling below 1 on March 24. Two homogeneous groups of epidemic curves were identified among regions, mainly differentiated by the magnitude of the daily incidence rate and the evolution of the Rt in the period prior to lockdown. However, irrespectively of the previous trend, the lockdown was followed by a steep decrease in the number of cases starting 6 days after its implementation. Interpretation: Our results confirm that the restrictive national lockdown efficiently reduced the progression of the epidemic in Spain during the first wave. This effect was similar in the two regional clusters, independent of the previous dynamics of the epidemic.N

The Psychological Science Accelerator: Advancing Psychology through a Distributed Collaborative Network

Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability

To which world regions does the valence-dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. Protocol registration: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1