Picturing Poverty: Developing Best Practices in Poverty Research and Map Making

This study investigates the dynamics of poverty by examining the historical construction of poverty definitions and measures in the United States. In addition, the paper explores how the definitions work to influence the measurements through an analysis of past and contemporary research on poverty and impoverished groups. The study also investigates how the definitions and measurements affect decision making through maps as communication tools by considering the recent Los Angeles General Plan Elements. Subsequently, the research breaks down the design of maps to quantify the gaps in information by using both Monmonier’s and Tufte’s guidelines in graphic design and map making best practice. The paper further examines the toolkits that are available to planners to improve definitions, measurements, and maps of poverty. The study concludes by offering planning recommendations to advance best practices for studying and displaying information about poverty

Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models.

Background There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain. Objective We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model. Data sources We searched Medline and EMBASE databases from inception to June 2020. Study eligibility criteria We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE. Methods We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism. Results We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76–0.82), calibration slope 0.98 (95% CI 0.86–1.13) and calibration-in-the-large –0.05 (95% CI –0.20 to 0.11). Conclusions The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE. Protocol registration PROSPERO (registration number CRD42020192602).pre-print270 K

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models

Background: There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain. Objective: We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model. Data sources: We searched Medline and EMBASE databases from inception to June 2020. Study eligibility criteria: We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE. Methods: We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism. Results: We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76–0.82), calibration slope 0.98 (95% CI 0.86–1.13) and calibration-in-the-large –0.05 (95% CI –0.20 to 0.11). Conclusions: The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE