0754 Meta-analysis of factors influencing new intramammary infection rate in natural exposure teat dip efficacy trials

2016 ◽  
Vol 94 (suppl_5) ◽  
pp. 362-362
Author(s):  
B. D. Enger ◽  
R. R. White ◽  
S. C. Nickerson ◽  
L. K. Fox
Keyword(s):  
Infection Rate ◽  
Meta Analysis ◽  
Intramammary Infection ◽  
Efficacy Trials ◽  
Factors Influencing ◽  
Natural Exposure

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Influenza vaccination coverage of population and the factors influencing influenza vaccination in mainland China: A meta-analysis

Vaccine ◽  
2018 ◽  
Vol 36 (48) ◽  
pp. 7262-7269 ◽  
Author(s):  
Qiang Wang ◽  
Na Yue ◽  
Mengyun Zheng ◽  
Donglei Wang ◽  
Chunxiao Duan ◽  
...  
Keyword(s):  
Influenza Vaccination ◽  
Vaccination Coverage ◽  
Meta Analysis ◽  
Mainland China ◽  
Factors Influencing

scholarly journals Retear rates after rotator cuff surgery: a systematic review and meta-analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Umile Giuseppe Longo ◽  
Arianna Carnevale ◽  
Ilaria Piergentili ◽  
Alessandra Berton ◽  
Vincenzo Candela ◽  
...  
Keyword(s):  
Rotator Cuff ◽  
Meta Analysis ◽  
Fatty Infiltration ◽  
Surgical Techniques ◽  
Postoperative Rehabilitation ◽  
Rehabilitation Protocol ◽  
Factors Influencing ◽  
Main Factors ◽  
Tear Size

Abstract Background Rotator cuff retear (RCR) is one of the main postoperative drawbacks. RCR can be considered a multifactorial issue, which causes are related either to biological than biomechanical factors. The aim of this study was to define the incidence of RCR after surgical treatment at different time points and to identify the main factors influencing the postoperative rotator cuff (RC) healing. Methods A systematic review and meta-analysis were performed following the PRISMA guidelines. A comprehensive search of the literature was carried out in July 2020, using PubMed and Cochrane Library databases. Only level 1 and 2 clinical evidence studies were included. Studies were included if patients with preoperative repairable full-thickness RC tears were treated surgically, and if studies reported postoperative RCR confirmed by imaging diagnostic. The association between timing of retear and follow-up time points were investigated using an inverse-variance method of pooling data. A subgroup meta-analysis was performed using the DerSimonian and Laird method for the estimation of the between-study variance, i.e., τ2. The association between retear rate after surgery and patients’ age, preoperative tear size, fatty infiltration, postoperative rehabilitation protocol, surgical techniques, and RC repairs was determined by expressing the effect measure in terms of odds ratio (OR) with 95% confidence interval (CI). The Mantel-Haenszel method with 95% CIs was used. Results Thirty-one articles were included in this study. The percentage of RCR after surgery was 15% at 3 months follow-up, 21% at 3–6 months follow-up, 16% at 6–12 months follow-up, 21% at 12–24 months follow-up, 16% at follow-up longer than 24 months. The main factors influencing RC healing are both patient-related (i.e., age, larger tear size, fatty infiltration) and not patient-related (i.e., postoperative rehabilitation protocol, surgical techniques, and procedures). Conclusions Postoperative RC healing is influenced by patient-related and non-patient-related factors. Further high-level clinical studies are needed to provide highly relevant clinical results.

Statistical Genetics

2018 ◽  
pp. 57-69 ◽  
Author(s):  
Till F. M. Andlauer ◽  
Bertram Müller-Myhsok ◽  
Stephan Ripke
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Meta Analysis ◽  
Power Calculation ◽  
Genome Wide Association Studies ◽  
Psychiatric Genetics ◽  
Factors Influencing ◽  
Genome Wide ◽  
Basic Concepts ◽  
Study Designs

Over more than the last decade, hypothesis-free genome-wide association studies (GWAS) have been widely used to detect genetic factors influencing phenotypes of interest. The basic principle of GWAS has been unchanged since the beginning: a series of univariate tests is conducted on all genetic variants available across the genome. We present study designs and commonly used methods for genome-wide studies, with a focus on the analysis of common variants. The basic concepts required for an application of GWAS in psychiatric genetics are introduced, from power calculation to meta-analysis. This chapter will help the reader in gaining the knowledge required for participation in and realization of GWAS of both qualitative and quantitative traits.

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