Assessing Systematic Reviews and Clinical Guidelines

2007 ◽  
pp. 73-82 ◽  
Author(s):  
Geraldine Macdonald
10.2196/22422 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e22422
Author(s):  
Tomohide Yamada ◽  
Daisuke Yoneoka ◽  
Yuta Hiraike ◽  
Kimihiro Hino ◽  
Hiroyoshi Toyoshiba ◽  
...  

Background Performing systematic reviews is a time-consuming and resource-intensive process. Objective We investigated whether a machine learning system could perform systematic reviews more efficiently. Methods All systematic reviews and meta-analyses of interventional randomized controlled trials cited in recent clinical guidelines from the American Diabetes Association, American College of Cardiology, American Heart Association (2 guidelines), and American Stroke Association were assessed. After reproducing the primary screening data set according to the published search strategy of each, we extracted correct articles (those actually reviewed) and incorrect articles (those not reviewed) from the data set. These 2 sets of articles were used to train a neural network–based artificial intelligence engine (Concept Encoder, Fronteo Inc). The primary endpoint was work saved over sampling at 95% recall (WSS@95%). Results Among 145 candidate reviews of randomized controlled trials, 8 reviews fulfilled the inclusion criteria. For these 8 reviews, the machine learning system significantly reduced the literature screening workload by at least 6-fold versus that of manual screening based on WSS@95%. When machine learning was initiated using 2 correct articles that were randomly selected by a researcher, a 10-fold reduction in workload was achieved versus that of manual screening based on the WSS@95% value, with high sensitivity for eligible studies. The area under the receiver operating characteristic curve increased dramatically every time the algorithm learned a correct article. Conclusions Concept Encoder achieved a 10-fold reduction of the screening workload for systematic review after learning from 2 randomly selected studies on the target topic. However, few meta-analyses of randomized controlled trials were included. Concept Encoder could facilitate the acquisition of evidence for clinical guidelines.


2017 ◽  
Vol 33 (4) ◽  
pp. 534-540 ◽  
Author(s):  
James D. Chambers ◽  
Cayla J. Saret ◽  
Jordan E. Anderson ◽  
Patricia A. Deverka ◽  
Michael P. Douglas ◽  
...  

Objectives: The aim of this study was to examine the evidence payers cited in their coverage policies for multi-gene panels and sequencing tests (panels), and to compare these findings with the evidence payers cited in their coverage policies for other types of medical interventions.Methods: We used the University of California at San Francisco TRANSPERS Payer Coverage Registry to identify coverage policies for panels issued by five of the largest US private payers. We reviewed each policy and categorized the evidence cited within as: clinical studies, systematic reviews, technology assessments, cost-effectiveness analyses (CEAs), budget impact studies, and clinical guidelines. We compared the evidence cited in these coverage policies for panels with the evidence cited in policies for other intervention types (pharmaceuticals, medical devices, diagnostic tests and imaging, and surgical interventions) as reported in a previous study.Results: Fifty-five coverage policies for panels were included. On average, payers cited clinical guidelines in 84 percent of their coverage policies (range, 73–100 percent), clinical studies in 69 percent (50–87 percent), technology assessments 47 percent (33–86 percent), systematic reviews or meta-analyses 31 percent (7–71 percent), and CEAs 5 percent (0–7 percent). No payers cited budget impact studies in their policies. Payers less often cited clinical studies, systematic reviews, technology assessments, and CEAs in their coverage policies for panels than in their policies for other intervention types. Payers cited clinical guidelines in a comparable proportion of policies for panels and other technology types.Conclusions: Payers in our sample less often cited clinical studies and other evidence types in their coverage policies for panels than they did in their coverage policies for other types of medical interventions.


2016 ◽  
Vol 10 (2) ◽  
pp. 1-2
Author(s):  
JP Neilson

In 2013, a workshop was held in Kathmandu that explored systematic reviews – what they are, how they are developed, how they are used in evidencebased clinical guidelines, and how they can inform the clinical research agenda. The workshop was funded by the Gates Foundation through FIGO, and organised by the Nepal Society of Obstetricians and Gynaecologists.


2016 ◽  
Vol 1 ◽  
pp. 2057178X1665830 ◽  
Author(s):  
Naeema M Al Bulushi ◽  
Lorna MD Macpherson ◽  
Heather Worlledge-Andrew ◽  
John Gibson ◽  
Alastair J Ross ◽  
...  

2013 ◽  
Vol 22 (Suppl 1) ◽  
pp. A69.3-A70
Author(s):  
D Geba ◽  
W Chan ◽  
M Moreno ◽  
T Pearson

2017 ◽  
Vol 2 (2) ◽  
pp. 020233
Author(s):  
Vitaliy Bezsheiko

Overweight and obesity are amongst most common problems of our time and have a significant negative impact on somatic health. Recently have been published several systematic reviews and meta-analysis that evaluated the effectiveness of various methods for overweight and obesity treatment. These studies are not yet included in the clinical guidelines, but might contain important results.


2021 ◽  
Author(s):  
Christian Gunge Riberholt ◽  
Markus Harboe Olsen ◽  
Joachim Birch Milan ◽  
Christian Gluud

Abstract Background: Adequately conducted systematic reviews with meta-analyses are considered the highest level of evidence and thus directly defines many clinical guidelines. However, the risk of type I and II errors in meta-analyses are substantial. Trial Sequential Analysis is a method for controlling these risks. Erroneous use of the method might lead to research waste or misleading conclusions. Methods: The current protocol describes a systematic review aimed to identify common and major mistakes and errors in the use of Trial Sequential Analysis by evaluating published systematic reviews and meta-analyses that include this method. We plan to include all studies using Trial Sequential Analysis published from 2018 to 2021, an estimated 400 to 600 publications. We will search Medical Literature Analysis and Retrieval System Online (MEDLINE) and the Cochrane Database of Systematic Reviews (CDSR), including studies with all types of participants, interventions, and outcomes. The search will begin in July 2021. Two independent reviewers will screen titles and abstracts, include relevant full text articles, extract data from the studies into a predefined checklist, and evaluate the methodological quality of the study using the AMSTAR 2 (Assessing the methodological quality of systematic reviews). Discussion: This protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P). The identified mistakes and errors will form the basis of a reviewed guideline for the use of Trial Sequential Analysis. Appropriately controlling for type I and II errors might reduce research waste and improve quality and precision of the evidence that clinical guidelines are based upon.


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