scholarly journals Takayasu Arteritis: a Systematic Review and Meta‐Analysis of Test Accuracy and Benefits and Harms of Common Treatments

2021 ◽  
Vol 3 (2) ◽  
pp. 80-90
Author(s):  
Anisha B. Dua ◽  
Mohamad A. Kalot ◽  
Nedaa M. Husainat ◽  
Kevin Byram ◽  
Jason M. Springer ◽  
...  
Keyword(s):  
Systematic Review ◽  
Takayasu Arteritis ◽  
Meta Analysis ◽  
Test Accuracy

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Disease-modifying anti-rheumatic drugs for the management of Takayasu arteritis—a systematic review and meta-analysis

2021 ◽  
Author(s):  
Durga Prasanna Misra ◽  
Upendra Rathore ◽  
Pallavi Patro ◽  
Vikas Agarwal ◽  
Aman Sharma
Keyword(s):  
Systematic Review ◽  
Takayasu Arteritis ◽  
Meta Analysis ◽  
Disease Modifying

scholarly journals Evaluating the diagnostic test accuracy of molecular xenomonitoring methods for characterising community burden of lymphatic filariasis

2021 ◽  
Author(s):  
Joseph Pryce ◽  
Lisa J Reimer
Keyword(s):  
Systematic Review ◽  
Lymphatic Filariasis ◽  
Meta Analysis ◽  
Test Accuracy ◽  
Sample Sizes ◽  
Pathogen Dna ◽  
Low Sensitivity ◽  
The Relationship ◽  
Molecular Xenomonitoring ◽  
Strong Linear Relationship

Abstract Background Molecular xenomonitoring (MX), the detection of pathogen DNA in mosquitoes, is a recommended approach to support lymphatic filariasis (LF) elimination efforts. Potential roles of MX include detecting presence of LF in communities and quantifying progress towards elimination of the disease. However, the relationship between MX results and human prevalence is poorly understood. Methods :We conducted a systematic review and meta-analysis from all previously conducted studies that reported the prevalence of filarial DNA in wild-caught mosquitoes (MX rate) and the corresponding prevalence of microfilaria (mf) in humans. We calculated a pooled estimate of MX sensitivity for detecting positive communities at a range of mf prevalence values and mosquito sample sizes. We conducted a linear regression to evaluate the relationship between mf prevalence and MX rate. Results We identified 24 studies comprising 144 study communities. MX had an overall sensitivity of 98.3% (95% CI 41.5, 99.9%) and identified 28 positive communities that were negative in the mf survey. Low sensitivity in some studies was attributed to small mosquito sample sizes (<1,000) and very low mf prevalence (<0.25%). Human mf prevalence and mass drug administration status accounted for approximately half of the variation in MX rate (R 2 = 0.49, p<0.001). Data from longitudinal studies showed that, within a given study area, there is a strong linear relationship between MX rate and mf prevalence (R 2 = 0.78, p < 0.001). Conclusion MX shows clear potential as tool for detecting communities where LF is present and as a predictor of human mf prevalence.

We need to do better: A systematic review and meta-analysis of diagnostic test accuracy of restless legs syndrome screening instruments

2021 ◽  
Vol 58 ◽  
pp. 101461
Author(s):  
Stephany Fulda ◽  
Richard P. Allen ◽  
Christopher J. Earley ◽  
Birgit Högl ◽  
Diego Garcia-Borreguero ◽  
...  
Keyword(s):  
Systematic Review ◽  
Diagnostic Test ◽  
Restless Legs Syndrome ◽  
Meta Analysis ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Screening Instruments ◽  
Restless Legs
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