scholarly journals A Research Protocol for a Systematic Review of Automatic Literature Screening in Medical Evidence Synthesis

2020 ◽  
Author(s):  
Yuelun Zhang ◽  
Siyu Liang ◽  
Yunying Feng ◽  
Qing Wang ◽  
Feng Sun ◽  
...  
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Evidence Based ◽  
Medical Evidence ◽  
Registration Number ◽  
Evidence Collection ◽  
Automatic Methods ◽  
And Performance

Abstract Background: Systematic review is an indispensable tool for optimal evidence collection and evaluation in evidence-based medicine. However, the explosive increase of the original literatures makes it difficult to accomplish critical appraisal and regular update. Artificial intelligence (AI) algorithms have been applied to automate the literature screening procedure in medical systematic reviews. In these studies, different algorithms were used and results with great variance were reported. It is therefore imperative to systematically review and analyse the developed automatic methods for literature screening and their effectiveness reported in current studies.Methods: An electronic search will be conducted using PubMed, Embase and IEEE Xplore Digital Library databases, as well as literatures found through supplementary search in Google scholar, on automatic methods for literature screening in systematic reviews. Two reviewers will independently conduct the primary screening of the articles and data extraction, in which nonconformities will be solved by discussion with a methodologist. Data will be extracted from eligible studies, including the basic characteristics of study, the information of training set and validation set, the function and performance of AI algorithms, and summarised in a table. The risk of bias and applicability of the eligible studies will be assessed by the two reviewers independently based on Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Quantitative analyses, if appropriate, will also be performed.Discussion: Automating systematic review process is of great help in reducing workload in evidence-based practice. Results from this systematic review will provide essential summary of the current development of AI algorithms for automatic literature screening in medical evidence synthesis, and help to inspire further studies in this field. Registration: PROSPERO registration number CRD42020170815 (28 April 2020).

scholarly journals Automation of literature screening using machine learning in medical evidence synthesis: a diagnostic test accuracy systematic review protocol

2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuelun Zhang ◽  
Siyu Liang ◽  
Yunying Feng ◽  
Qing Wang ◽  
Feng Sun ◽  
...  
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Digital Library ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Test Accuracy ◽  
Evidence Based ◽  
Medical Evidence ◽  
Evidence Collection ◽  
Automatic Methods

Abstract Background Systematic review is an indispensable tool for optimal evidence collection and evaluation in evidence-based medicine. However, the explosive increase of the original literatures makes it difficult to accomplish critical appraisal and regular update. Artificial intelligence (AI) algorithms have been applied to automate the literature screening procedure in medical systematic reviews. In these studies, different algorithms were used and results with great variance were reported. It is therefore imperative to systematically review and analyse the developed automatic methods for literature screening and their effectiveness reported in current studies. Methods An electronic search will be conducted using PubMed, Embase, ACM Digital Library, and IEEE Xplore Digital Library databases, as well as literatures found through supplementary search in Google scholar, on automatic methods for literature screening in systematic reviews. Two reviewers will independently conduct the primary screening of the articles and data extraction, in which nonconformities will be solved by discussion with a methodologist. Data will be extracted from eligible studies, including the basic characteristics of study, the information of training set and validation set, and the function and performance of AI algorithms, and summarised in a table. The risk of bias and applicability of the eligible studies will be assessed by the two reviewers independently based on Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Quantitative analyses, if appropriate, will also be performed. Discussion Automating systematic review process is of great help in reducing workload in evidence-based practice. Results from this systematic review will provide essential summary of the current development of AI algorithms for automatic literature screening in medical evidence synthesis and help to inspire further studies in this field. Systematic review registration PROSPERO CRD42020170815 (28 April 2020).

scholarly journals How do middle-aged and older adults with chronic hip pain view their health problem and its care? A protocol for a systematic review and qualitative evidence synthesis

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e053084
Author(s):  
Travis Haber ◽  
Rana S Hinman ◽  
Fiona Dobson ◽  
Samantha Bunzli ◽  
Michelle Hall
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Hip Pain ◽  
Qualitative Evidence Synthesis ◽  
Middle Aged ◽  
Qualitative Evidence ◽  
Prisma Statement ◽  
Registration Number

IntroductionChronic hip pain in middle-aged and older adults is common and disabling. Patient-centred care of chronic hip pain requires a comprehensive understanding of how people with chronic hip pain view their health problem and its care. This paper outlines a protocol to synthesise qualitative evidence of middle-aged and older adults' views, beliefs, expectations and preferences about their chronic hip pain and its care.Methods and analysisWe will perform a qualitative evidence synthesis using a framework approach. We will conduct this study in accord with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement and the Enhancing Transparency in Reporting the synthesis of Qualitative research checklist. We will search MEDLINE, CINAHL, The Cochrane Central Register of Controlled Trials, EMBASE and PsycINFO using a comprehensive search strategy. A priori selection criteria include qualitative studies involving samples with a mean age over 45 and where 80% or more have chronic hip pain. Two or more reviewers will independently screen studies for eligibility, assess methodological strengths and limitations using the Critical Appraisal Skills Programme qualitative studies checklist, perform data extraction and synthesis and determine ratings of confidence in each review finding using the Grading of Recommendations Assessment, Development and Evaluation—Confidence in the Evidence from Reviews of Qualitative research approach. Data extraction and synthesis will be guided by the Common-Sense Model of Self-Regulation. All authors will contribute to interpreting, refining and finalising review findings. This protocol is registered on PROSPERO and reported according to the PRISMA Statement for Protocols (PRISMA-P) checklist.Ethics and disseminationEthics approval is not required for this systematic review as primary data will not be collected. The findings of the review will be disseminated through publication in an academic journal and scientific conferences.PROSPERO registration numberPROSPERO registration number: CRD42021246305.

scholarly journals The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews

2019 ◽  
Vol 3 ◽  
pp. 157
Author(s):  
Fala Cramond ◽  
Alison O'Mara-Eves ◽  
Lee Doran-Constant ◽  
Andrew SC Rice ◽  
Malcolm Macleod ◽  
...  
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Statistical Data ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Online Application ◽  
Web Based ◽  
Graphical Data ◽  
Data Extraction Tool ◽  
Increase In Accuracy

Background: The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process. Methods: To mitigate some of the above problems we integrated and customised two existing JavaScript libraries to create a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison to standard practice. Results: We found that the customised graphical data extraction tool is not inferior to users’ prior preferred current approaches. Our study was not designed to show superiority, but suggests that there may be a saving in time of around 6 minutes per graph, accompanied by a substantial increase in accuracy. Conclusions: Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.

scholarly journals Promoting evidence-based practice: training health professionals for the evidence synthesis

2021 ◽  
Vol 55 ◽  
Author(s):  
Daniela Filipa Batista Cardoso ◽  
Diana Gabriela Simões Marques Santos ◽  
Joana Filipa Cunha Rodrigues ◽  
Nichole Bento ◽  
Rogério Manuel Clemente Rodrigues ◽  
...  
Keyword(s):  
Systematic Review ◽  
Training Program ◽  
Systematic Reviews ◽  
Health Professionals ◽  
Evidence Based Practice ◽  
Evidence Synthesis ◽  
Evidence Based ◽  
Centre Of Excellence ◽  
Satisfaction Evaluation ◽  
Students Satisfaction

ABSTRACT Objective: To report the experience of the Portugal Centre For Evidence Based Practice (PCEBP): a JBI Centre of Excellence in the training of health professionals, researchers, and professors in the Comprehensive Systematic Review Training Program, a course on Evidence Synthesis, specifically on Systematic Literature Reviews. Method: This article aims to report the experience of the Portugal Centre For Evidence Based Practice: a JBI Centre of Excellence in the implementation of the Comprehensive Systematic Review Training Program that trains health professionals, researchers, and teachers to develop Systematic Reviews, according to the JBI approach. Results: By the end of 2020, 11 editions of the course had been developed with 136 participants from different educational and health institutions, from different countries. As a result of the training of these participants, 13 systematic reviews were published in JBI Evidence Synthesis and 10 reviews were published in other journals. Conclusion: The reported results and the students’ satisfaction evaluation allow us to emphasize the relevance of the course for health professionals training on evidence synthesis.

scholarly journals Evaluating methodological quality of Prognostic models Including Patient-reported HeAlth outcomes iN oncologY (EPIPHANY): a systematic review protocol

BMJ Open ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. e025054 ◽  
Author(s):  
Nina Deliu ◽  
Francesco Cottone ◽  
Gary S Collins ◽  
Amélie Anota ◽  
Fabio Efficace
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Methodological Quality ◽  
Data Extraction ◽  
Prognostic Models ◽  
Published Data ◽  
Evidence Based ◽  
Patient Reported ◽  
Data Extraction Form

IntroductionWhile there is mounting evidence of the independent prognostic value of patient-reported outcomes (PROs) for overall survival (OS) in patients with cancer, it is known that the conduct of these studies may hold a number of methodological challenges. The aim of this systematic review is to evaluate the quality of published studies in this research area, in order to identify methodological and statistical issues deserving special attention and to also possibly provide evidence-based recommendations.Methods and analysisAn electronic search strategy will be performed in PubMed to identify studies developing or validating a prognostic model which includes PROs as predictors. Two reviewers will independently be involved in data collection using a predefined and standardised data extraction form including information related to study characteristics, PROs measures used and multivariable prognostic models. Studies selection will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, with data extraction form using fields from the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist for multivariable models. Methodological quality assessment will also be performed and will be based on prespecified domains of the CHARMS checklist. As a substantial heterogeneity of included studies is expected, a narrative evidence synthesis will also be provided.Ethics and disseminationGiven that this systematic review will use only published data, ethical permissions will not be required. Findings from this review will be published in peer-reviewed scientific journals and presented at major international conferences. We anticipate that this review will contribute to identify key areas of improvement for conducting and reporting prognostic factor analyses with PROs in oncology and will lay the groundwork for developing future evidence-based recommendations in this area of research.Prospero registration numberCRD42018099160.

scholarly journals The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews

2018 ◽  
Vol 3 ◽  
pp. 157
Author(s):  
Fala Cramond ◽  
Alison O'Mara-Eves ◽  
Lee Doran-Constant ◽  
Andrew SC Rice ◽  
Malcolm Macleod ◽  
...  
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Statistical Data ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Online Application ◽  
Web Based ◽  
Graphical Data ◽  
Data Extraction Tool ◽  
Increase In Accuracy

Background: The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process. Methods: To mitigate some of the above problems we developed a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison to standard practice. Results: We found that our new graphical data extraction tool is not inferior to users’ prior preferred current approaches. Our study was not designed to show superiority, but suggests that there may be a saving in time of around 6 minutes per graph, accompanied by a substantial increase in accuracy. Conclusions: Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.

scholarly journals Acting in the Face of Evidentiary Ambiguity, Bias, and Absence Arising from Systematic Reviews in Applied Environmental Science

2021 ◽  
Author(s):  
Trina Rytwinski ◽  
Steven J Cooke ◽  
Jessica J Taylor ◽  
Dominique Roche ◽  
Paul A Smith ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Systematic Reviews ◽  
Environmental Science ◽  
Evidence Synthesis ◽  
Empirical Studies ◽  
Synthesis Method ◽  
Evidence Base ◽  
Evidence Based ◽  
Evidence Based Decision Making

Evidence-based decision-making often depends on some form of a synthesis of previous findings. There is growing recognition that systematic reviews, which incorporate a critical appraisal of evidence, are the gold standard synthesis method in applied environmental science. Yet, on a daily basis, environmental practitioners and decision-makers are forced to act even if the evidence base to guide them is insufficient. For example, it is not uncommon for a systematic review to conclude that an evidence base is large but of low reliability. There are also instances where the evidence base is sparse (e.g., one or two empirical studies on a particular taxa or intervention), and no additional evidence arises from a systematic review. In some cases, the systematic review highlights considerable variability in the outcomes of primary studies, which in turn generates ambiguity (e.g., potentially context specific). When the environmental evidence base is ambiguous, biased, or lacking of new information, practitioners must still make management decisions. Waiting for new, higher validity research to be conducted is often unrealistic as many decisions are urgent. Here, we identify the circumstances that can lead to ambiguity, bias, and the absence of additional evidence arising from systematic reviews and provide practical guidance to resolve or handle these scenarios when encountered. Our perspective attempts to highlight that, with evidence synthesis, there may be a need to balance the spirit of evidence-based decision-making and the practical reality that management and conservation decisions and action is often time sensitive.

scholarly journals New meniscal tears after ACL injury: what is the risk? A systematic review protocol

2017 ◽  
Vol 52 (6) ◽  
pp. 386-386 ◽  
Author(s):  
Guri Ranum Ekås ◽  
Clare Ardern ◽  
Hege Grindem ◽  
Lars Engebretsen
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Demographic Data ◽  
Acl Injury ◽  
Risk Of Bias ◽  
Meniscal Tears ◽  
Best Evidence Synthesis

BackgroundSecondary meniscal tears after ACL injuries increase the risk of knee osteoarthritis. The current literature on secondary meniscal injuries after ACL injury is not consistent and may have methodological shortcomings. This protocol describes the methods of a systematic review investigating the rate of secondary meniscal injuries in children and adults after treatment (operative or non-operative) for ACL injury.MethodsWe will search electronic databases (Embase, Ovid Medline, Cochrane, CINAHL (Cumulative Index to Nursing and Allied Health Literature), SPORTDiscus, PEDro and Google Scholar) from database inception. Extracted data will include demographic data, methodology, intervention details and patient outcomes. Risk of bias will be assessed using the Newcastle Ottawa checklist for cohort studies. Article screening, eligibility assessment, risk of bias assessment and data extraction will be performed in duplicate by independent reviewers. A proportion meta-analysis will be performed if studies are homogeneous (I2<75%). If meta-analysis is precluded, data will be synthesised descriptively using best-evidence synthesis. The strength of recommendations and quality of evidence will be assessed using the Grading of Recommendations Assessment Development and Evaluation working group methodology.Ethics and disseminationThis protocol is written according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses, and was registered in the International Prospective Register of Systematic Reviews on 22 March 2016.Trial registration numberCRD42016036788.

scholarly journals Methodology for Evidence Synthesis and Development of Comprehensive Evidence-Based Guidelines for Interventional Techniques in Chronic Spinal Pain

2021 ◽  
Vol S1;24 (1;S1) ◽  
pp. S1-S26
Keyword(s):  
Systematic Review ◽  
Clinical Practice ◽  
Systematic Reviews ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Observational Studies ◽  
Evidence Synthesis ◽  
Evidence Based ◽  
Interventional Techniques

BACKGROUND: The re-engineered definition of clinical guidelines in 2011 from the IOM (Institute of Medicine) states, “clinical practice guidelines are statements that include recommendations intended to optimize patient care that is informed by a systematic review of evidence and an assessment of the benefit and harms of alternative care options.” The revised definition distinguishes between the term “clinical practice guideline” and other forms of clinical guidance derived from widely disparate development processes, such as consensus statements, expert advice, and appropriate use criteria. OBJECTIVE: To assess the literature and develop methodology for evidence synthesis and development of comprehensive evidence-based guidelines for interventional techniques in chronic spinal pain. METHODS: A systematic review of the literature including methodology of guideline development encompassing GRADE approach for guidance on evidence synthesis with recommendations. RESULTS: Some of the many factors described in 2011 continue as of 2020 and impede the development of clinical practice guidelines. These impediments include biases due to a variety of conflicts and confluence of interest, inappropriate and poor methodological quality, poor writing and ambiguous presentation, projecting a view that these are not applicable to individual patients or too restrictive with the elimination of clinician autonomy, and overzealous and inappropriate recommendations, either positive, negative, or non-committal. Thus, ideally, a knowledgeable, multidisciplinary panel of experts with true lack of bias and confluence of interest must develop guidelines based on a systematic review of the existing evidence. This manuscript describes evidence synthesis from observational studies, various types of randomized controlled trials (RCTs), and, finally, methodological and reporting quality of systematic reviews. The manuscript also describes various methods utilized in the assessment of the quality of observational studies, diagnostic accuracy studies, RCTs, and systematic reviews. LIMITATIONS: Paucity of publications with appropriate evidence synthesis methodology in reference to interventional techniques. CONCLUSION: This review described comprehensive evidence synthesis derived from systematic reviews, including methodologic quality and bias measurement. The manuscript described various methods utilized in the assessment of the quality of the systematic reviews, RCTs, diagnostic accuracy studies, and observational studies. KEY WORDS: Evidence-based medicine (EBM), interventional pain management, evidence synthesis, methodological quality assessment, conflict of interest, confluence of interest, comparative effectiveness research (CER), clinical practice guidelines, systematic reviews, meta-analysis

scholarly journals What does my neighbourhood have to do with my weight? A protocol for systematic review and meta-analysis of the association between neighbourhood socioeconomic status and body weight

BMJ Open ◽  
2017 ◽  
Vol 7 (9) ◽  
pp. e017567
Author(s):  
Shimels Hussien Mohammed ◽  
Mulugeta Molla Birhanu ◽  
Tesfamichael Awoke Sissay ◽  
Tesfa Dejenie Habtewold ◽  
Balewgizie Sileshi Tegegn ◽  
...  
Keyword(s):  
Systematic Review ◽  
Socioeconomic Status ◽  
Data Extraction ◽  
Meta Analysis ◽  
Cross Sectional ◽  
Registration Number ◽  
Newcastle Ottawa Scale ◽  
Neighbourhood Socioeconomic Status ◽  
Using Data

IntroductionIndividuals living in poor neighbourhoods are at a higher risk of overweight/obesity. There is no systematic review and meta-analysis study on the association of neighbourhood socioeconomic status (NSES) with overweight/obesity. We aimed to systematically review and meta-analyse the existing evidence on the association of NSES with overweight/obesity.Methods and analysisCross-sectional, case–control and cohort studies published in English from inception to 15 May 2017 will be systematically searched using the following databases: PubMed, EMBASE, Web of Sciences and Google Scholar. Selection, screening, reviewing and data extraction will be done by two reviewers, independently and in duplicate. The Newcastle–Ottawa Scale (NOS) will be used to assess the quality of evidence. Publication bias will be checked by visual inspection of funnel plots and Egger’s regression test. Heterogeneity will be checked by Higgins’s method (I2statistics). Meta-analysis will be done to estimate the pooled OR. Narrative synthesis will be performed if meta-analysis is not feasible due to high heterogeneity of studies.Ethics and disseminationEthical clearance is not required as we will be using data from published articles. Findings will be communicated through a publication in a peer-reviewed journal and presentations at professional conferences.PROSPERO registration numberCRD42017063889.

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