Dataset Information

Development, testing and use of data extraction forms in systematic reviews: a review of methodological guidance.

ABSTRACT: BACKGROUND:Data extraction forms link systematic reviews with primary research and provide the foundation for appraising, analysing, summarising and interpreting a body of evidence. This makes their development, pilot testing and use a crucial part of the systematic reviews process. Several studies have shown that data extraction errors are frequent in systematic reviews, especially regarding outcome data. METHODS:We reviewed guidance on the development and pilot testing of data extraction forms and the data extraction process. We reviewed four types of sources: 1) methodological handbooks of systematic review organisations (SRO); 2) textbooks on conducting systematic reviews; 3) method documents from health technology assessment (HTA) agencies and 4) journal articles. HTA documents were retrieved in February 2019 and database searches conducted in December 2019. One author extracted the recommendations and a second author checked them for accuracy. Results are presented descriptively. RESULTS:Our analysis includes recommendations from 25 documents: 4 SRO handbooks, 11 textbooks, 5 HTA method documents and 5 journal articles. Across these sources the most common recommendations on form development are to use customized or adapted standardised extraction forms (14/25); provide detailed instructions on their use (10/25); ensure clear and consistent coding and response options (9/25); plan in advance which data are needed (9/25); obtain additional data if required (8/25); and link multiple reports of the same study (8/25). The most frequent recommendations on piloting extractions forms are that forms should be piloted on a sample of studies (18/25); and that data extractors should be trained in the use of the forms (7/25). The most frequent recommendations on data extraction are that extraction should be conducted by at least two people (17/25); that independent parallel extraction should be used (11/25); and that procedures to resolve disagreements between data extractors should be in place (14/25). CONCLUSIONS:Overall, our results suggest a lack of comprehensiveness of recommendations. This may be particularly problematic for less experienced reviewers. Limitations of our method are the scoping nature of the review and that we did not analyse internal documents of health technology agencies.

SUBMITTER: Buchter RB

PROVIDER: S-EPMC7574308 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development, testing and use of data extraction forms in systematic reviews: a review of methodological guidance.

Büchter Roland Brian RB Weise Alina A Pieper Dawid D

BMC medical research methodology 20201019 1

<h4>Background</h4>Data extraction forms link systematic reviews with primary research and provide the foundation for appraising, analysing, summarising and interpreting a body of evidence. This makes their development, pilot testing and use a crucial part of the systematic reviews process. Several studies have shown that data extraction errors are frequent in systematic reviews, especially regarding outcome data.<h4>Methods</h4>We reviewed guidance on the development and pilot testing of data e ...[more]

PMID: 33076832

Similar Datasets

Project description:BackgroundSystematic reviews (SRs) with or without meta-analyses (MAs) are widely used in resolving questions in various healthcare areas (such as, traditional Chinese medicine, public health and surgery), and they are the cornerstone of evidence-based healthcare. However, the reliability of SRs is typically influenced by their methodological quality. AMSTAR (A Measurement Tool to Assess Systematic Reviews) and AMSTAR-2 tools can assess the methodological quality of SRs, and the use of AMSTAR has been investigated. However, AMSTAR-2 is now widely used to evaluate the methodological quality of SRs, but the use of AMSTAR-2 for determining the methodological quality of SRs has not yet been investigated and assessed thoroughly. Thus, we designed the present study to investigate the use of AMSTAR-2 in studies that assessed the methodological quality of a sample of SRs with the AMSTAR-2 and provide references to potential users of AMSTAR-2 tool.MethodsFour commonly used electronic databases including PubMed, EmBase, the Cochrane Library, and Web of Science will be searched following a comprehensive search strategy to identify and retrieve studies that have used AMSTAR-2 tool for evaluating the methodological quality of SRs. Two independent authors will retrieve bibliometric information and methodological data, including all author names, time of publication, and journal names, whether a specific score value was given for each item, and whether overall quality assessment was performed. Descriptive statistical analyses will be used to present the study results, e.g., frequencies and percentages, mean and standard deviation (SD) or median and interquartile range (IQR). In addition, subgroup analyses will be performed to identify the methodological differences (e.g., the reporting of study designs included in SRs) between overviews and methodological studies. The risk ratio (RR) with 95% confidence interval (95% CI) will be calculated to measure the methodological differences. Cytoscape 3.7.1 software tool will be used to construct collaboration network maps. Further, Microsoft Office Excel 2016 and Stata 12.0 will be used to manage and analyze data.DiscussionThe results of this study will identify any gaps in the use of AMSTAR-2 and important bibliometric features, such as active researchers and journals, provide guidance to researchers in various healthcare areas (such as, traditional Chinese medicine and public health) for using AMSTAR-2 tool and help them in developing cooperation and submitting their manuscripts.

Project description:BackgroundThe screening process for systematic reviews and meta-analyses in medical research is a labor-intensive and time-consuming task. While machine learning and deep learning have been applied to facilitate this process, these methods often require training data and user annotation. This study aims to assess the efficacy of ChatGPT, a large language model based on the Generative Pretrained Transformers (GPT) architecture, in automating the screening process for systematic reviews in radiology without the need for training data.MethodsA prospective simulation study was conducted between May 2nd and 24th, 2023, comparing ChatGPT's performance in screening abstracts against that of general physicians (GPs). A total of 1198 abstracts across three subfields of radiology were evaluated. Metrics such as sensitivity, specificity, positive and negative predictive values (PPV and NPV), workload saving, and others were employed. Statistical analyses included the Kappa coefficient for inter-rater agreement, ROC curve plotting, AUC calculation, and bootstrapping for p-values and confidence intervals.ResultsChatGPT completed the screening process within an hour, while GPs took an average of 7-10 days. The AI model achieved a sensitivity of 95% and an NPV of 99%, slightly outperforming the GPs' sensitive consensus (i.e., including records if at least one person includes them). It also exhibited remarkably low false negative counts and high workload savings, ranging from 40 to 83%. However, ChatGPT had lower specificity and PPV compared to human raters. The average Kappa agreement between ChatGPT and other raters was 0.27.ConclusionsChatGPT shows promise in automating the article screening phase of systematic reviews, achieving high sensitivity and workload savings. While not entirely replacing human expertise, it could serve as an efficient first-line screening tool, particularly in reducing the burden on human resources. Further studies are needed to fine-tune its capabilities and validate its utility across different medical subfields.

Dataset Information

Development, testing and use of data extraction forms in systematic reviews: a review of methodological guidance.

Publications

Development, testing and use of data extraction forms in systematic reviews: a review of methodological guidance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets