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39 results for “Risk of BIas”
Data from: The effect of sex-biased dispersal on opposite-sexed spatial genetic structure and inbreeding risk
Natal sex-biased dispersal has long been thought to reduce the risk of inbreeding by spatially separating opposite-sexed kin. Yet, comprehensive and quantitative evaluations of this hypothesis are lacking. In this study, we quantified the effectiveness of sex-biased dispersal as an inbreeding avoidance strategy by combining spatially explicit simulations and empirical data. We quantified the extent of kin clustering by measuring the degree of spatial autocorrelation among opposite-sexed individuals (FM structure). This allowed us to systematically explore how the extent of sex-biased dispersal, generational overlap, and mate searching distance, influenced both kin clustering, and the resulting inbreeding in the absence of complementary inbreeding avoidance strategies. Simulations revealed that when sex-biased dispersal was limited, positive FM genetic structure developed quickly and increased as the mate searching distance decreased or as generational overlap increased. Interestingly, complete long-range sex-biased dispersal did not prevent the development of FM genetic structure when generations overlapped. We found a very strong correlation between FM genetic structure and both FIS under random mating, and pedigree-based measures of inbreeding. Thus, we show that the detection of FM genetic structure can be a strong indicator of inbreeding risk. Empirical data for two species with different life history strategies yielded patterns congruent with our simulations. Our study illustrates a new application of spatial genetic autocorrelation analysis that offers a framework for quantifying the risk of inbreeding that is easily extendable to other species. Furthermore, our findings provide other researchers with a context for interpreting observed patterns of opposite-sexed spatial genetic structure.
Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding
Purpose: This retrospective cross-sectional study aimed to identify opportunities for improvement in food and nutrition research by examining risk of bias (ROB) domains. Methods: Rating were extracted from critical appraisal records for 5675 studies used in systematic reviews conducted by three organizations. Variables were as follows: ROB domains defined by the Cochrane Collaboration (Selection, Performance, Detection, Attrition, and Reporting), publication year, research type (intervention or observation) and specific design, funder, and overall quality rating (positive, neutral, or negative). Appraisal instrument questions were mapped to ROB domains. The kappa statistic was used to determine consistency when multiple ROB ratings were available. Binary logistic regression and multinomial logistic regression were used to predict overall quality and ROB domains. Findings: Studies represented a wide variety of research topics (clinical nutrition, food safety, dietary patterns, and dietary supplements) among 15 different research designs with a balance of intervention (49%) and observation (51%) types, published between 1930 and 2015 (64% between 2000-2009). Duplicate ratings (10%) were consistent (k=0.86-0.94). Selection and Performance domain criteria were least likely to be met (57.9% to 60.1%). Selection, Detection, and Performance ROB ratings predicted neutral or negative quality compared to positive quality (p<0.001). Funder, year, and research design were significant predictors of ROB. Some sources of funding predicted increased ROB (p<0.001) for Selection (Interventional: industry only and none/not reported; Observational: other only and none/not reported) and Reporting (Observational: university only and other only). Reduced ROB was predicted by combined and other-only funding for intervention research (p<0.005). Performance ROB domain ratings started significantly improving in 2000; others improved after 1990 (p<0.001). Research designs with higher ROB were nonrandomized intervention and time series designs compared to RCT and prospective cohort designs respectively (p<0.001). Conclusions: Opportunities for improvement in food and nutrition research are in the Selection, Performance, and Detection ROB domains.
Supplementary File_Nirmatrelvir_Risk of Bias Excel Tool (Version 2)
<p>Supplementary material (Risk of Bias Excel Tool (Version 2)) for the <strong>1st update</strong> of the Cochrane Review "Nirmatrelvir combined with ritonavir for preventing and treating COVID-19".</p>
Supplementary File_Nirmatrelvir_Risk of Bias Excel Tool (Version 1)
<p>Supplementary material (Risk of Bias Excel Tool (Version 1)) for the Cochrane Review "Nirmatrelvir combined with ritonavir for preventing and treating COVID-19".</p>
Risk-of-bias v.2 assessment with large language models
<p>See https://bitbucket.org/aimedtech/fewshot_rob for more information.</p>
Supplementary File_Antibiotics_Risk of Bias Excel Tool (Version 1)
<p>Supplementary material (Risk of Bias Excel Tool (Version 1)) for the Cochrane Review "Antibiotics for the treatment of COVID-19".</p>
Supplementary File_Antibiotics_Risk of Bias Excel Tool (Version 1)
<p>Supplementary material (Risk of Bias Excel Tool (Version 1)) for the Cochrane Review "Antibiotics for the treatment of COVID-19".</p>
Studies on the side effects of methylphenidate in adults diagnosed with ADHD. The risk of bias and study quality assessed with the Effective Public Health Practice Project's Quality Assessment Tool for Quantitative Studies (QATQS) was presented as the global rating for each publication (1 - strong, 2 - moderate, 3 - weak).
<p>The data set contains the analysis of adult studies on ADHD with respect on side effects of methylphenidate. </p>
Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding
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Data from: The effect of sex-biased dispersal on opposite-sexed spatial genetic structure and inbreeding risk
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Data from: The relationship between risk of bias criteria, research outcomes, and study sponsorship in a cohort of preclinical thiazolidinedione animal studies: a meta-analysis
Introduction: There is little evidence regarding the influence of conflicts of interest on preclinical research. This study examines whether industry sponsorship is associated with increased risks of bias and/or effect sizes of outcomes in published preclinical thiazolidinedione (TZD) studies. Methods: We identified preclinical TZD studies published between January 1, 1965, and November 14, 2012. Coders independently extracted information on study design criteria aimed at reducing bias, results for all relevant outcomes, sponsorship source and investigator financial ties from the 112 studies meeting the inclusion criteria. The average standardized mean difference (SMD) across studies was calculated for plasma glucose (efficacy outcome) and weight gain (harm outcome). In subgroup analyses, TZD outcomes were assessed by sponsorship source and risk of bias criteria. Results: Seven studies were funded by industry alone, 17 studies funded by both industry and non-industry, 49 studies funded by non-industry alone and 39 studies had no disclosures. None of the studies used sample size calculations, intention-to-treat analyses, blinding of investigators or concealment of allocation. Most studies reported favourable results (88 of 112) and conclusions (95 of 112) supporting TZD use. Efficacy estimates were significantly larger in six studies sponsored by industry alone (−3.41; 95% CI −5.21, −1.53; I2 = 93%) versus 42 studies sponsored by non-industry sources (−0.97; 95% CI −1.37, −0.56; I2 = 81%; p-value = 0.01). Harms estimates were significantly larger in four studies sponsored by industry alone (5.00; 95% CI 1.22, 8.77; I2 = 93%) versus 38 studies sponsored by non-industry sources (0.30; 95% CI −0.08, 0.68; I2 = 79%; p-value = 0.02). TZD efficacy and harms did not differ by disclosure of financial COIs or risks of bias. Conclusions: Industry-sponsored TZD animal studies have exaggerated efficacy and harms outcomes compared with studies funded by non-industry sources. There was poor reporting of COIs.
Risk of bias graph
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Risk of bias assessment for Corticosteroids for leptospirosis treatment
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Appendix 7 detailed risk of bias assessment of the included studies
<p>The file contains the detailed risk of bias assessment of the included studies in the master thesis "NON-PHARMACOLOGICAL INTERVENTIONS FOR THE MANAGEMENT OF PAIN IN PATIENTS WITH SPINAL CORD INJURY – A SYSTEMATIC REVIEW"</p>
Data from: The relationship between risk of bias criteria, research outcomes, and study sponsorship in a cohort of preclinical thiazolidinedione animal studies: a meta-analysis
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Data from: Risk of bias in reports of in vivo research: a focus for improvement
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Supplementary File_Ivermectin_Risk of Bias Excel Tool (Version 2)
<p>Supplementary material (Risk of Bias Excel Tool (Version 1)) for the updated Cochrane Review "Ivermectin for preventing and treating COVID-19".</p>
T helper 2-biased transcriptional profile predicts HIV envelope-specific polyfunctional CD4+ T cells that correlated with reduced risk of infection in RV144 trial
GEO Series GSE166945. Homo sapiens. 689 samples. Type: Expression profiling by high throughput sequencing.
Risk of Bias training data
<p>This is the "risk of bias" data used to train the RA-CNN model (https://arxiv.org/abs/1605.04469) in RobotReviewer.</p> <p>Model code available at: https://github.com/bwallace/rationale-CNN (specific commit: https://github.com/bwallace/rationale-CNN/tree/27bddddaecf358fa3886c4a702cc4c2230d24cb4)</p>
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.