A mapping exercise using automated techniques to develop a search strategy to identify systematic review tools.

automation evidence synthesis information retrieval literature search search strategy systematic reviews

Journal

Research synthesis methods
ISSN: 1759-2887
Titre abrégé: Res Synth Methods
Pays: England
ID NLM: 101543738

Informations de publication

Date de publication:
Nov 2023
Historique:
revised: 31 07 2023
received: 10 06 2022
accepted: 04 08 2023
medline: 8 11 2023
pubmed: 6 9 2023
entrez: 5 9 2023
Statut: ppublish

Résumé

The Systematic Review Toolbox aims provide a web-based catalogue of tools that support various tasks within the systematic review and wider evidence synthesis process. Identifying publications surrounding specific systematic review tools is currently challenging, leading to a high screening burden for few eligible records. We aimed to develop a search strategy that could be regularly and automatically run to identify eligible records for the SR Toolbox, thus reducing time on task and burden for those involved. We undertook a mapping exercise to identify the PubMed IDs of papers indexed within the SR Toolbox. We then used the Yale MeSH Analyser and Visualisation of Similarities (VOS) Viewer text-mining software to identify the most commonly used MeSH terms and text words within the eligible records. These MeSH terms and text words were combined using Boolean Operators into a search strategy for Ovid MEDLINE. Prior to the mapping exercise and search strategy development, 81 software tools and 55 'Other' tools were included within the SR Toolbox. Since implementation of the search strategy, 146 tools have been added. There has been an increase in tools added to the toolbox since the search was developed and its corresponding auto-alert in MEDLINE was originally set up. Developing a search strategy based on a mapping exercise is an effective way of identifying new tools to support the systematic review process. Further research could be conducted to help prioritise records for screening to reduce reviewer burden further and to adapt the strategy for disciplines beyond healthcare.

Identifiants

pubmed: 37669905
doi: 10.1002/jrsm.1665
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

874-881

Informations de copyright

© 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

Références

Borah R, Brown AW, Capers PL, Kaiser KA. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open. 2017;7(2):e012545.
Buscemi N, Hartling L, Vandermeer B, Tjosvold L, Klassen TP. Single data extraction generated more errors than double data extraction in systematic reviews. J Clin Epidemiol. 2006;59(7):697-703.
Ananiadou S, Rea B, Okazaki N, Procter R, Thomas J. Supporting systematic reviews using text mining. Soc Sci Comput Rev. 2009;27(4):509-523.
Bannach-Brown A, Przybyła P, Thomas J, et al. Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error. Syst Rev. 2019;8(1):1-12.
Begert D, Granek J, Irwin B, Brogly C. Using automation for repetitive work involved in a systematic review. CCDR. 2020;46(6):174-179.
Arno A, Elliott J, Wallace B, Turner T, Thomas J. The views of health guideline developers on the use of automation in health evidence synthesis. Syst Rev. 2021;10(1):1-10.
Munn Z, Brandt L, Kuijpers T, et al. Are systematic review and guideline development tools useful? A Guidelines International Network survey of user preferences. JBI Evid Implement. 2020;18(3):345-352.
Marshall C. Tool Support for Systematic Reviews in Software Engineering. Keele University; 2016 Available from: https://eprints.keele.ac.uk/id/eprint/2431/
Marshall C, Sutton A, O'Keefe H, Johnson E, eds. The Systematic Review Toolbox. 2022 Available from: http://www.systematicreviewtools.com/
Beller E et al. Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Syst Rev. 2018;7(1):1-7.
Cushing/Whitney Medical Library, Y.U. Yale MeSH Analyzer. 2021 [cited 2021 27 January]. Available from: https://mesh.med.yale.edu/
Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523-538.
Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5:210. doi:10.1186/s13643-016-0384-4
Johnson EE, O'Keefe H, Sutton A, Marshall C. The Systematic Review Toolbox: keeping up to date with tools to support evidence synthesis. Syst Rev. 2022;11(1):1-8.
Sutton A, Marshall C. Better, faster, stronger: how to find tools to expedite the systematic review process. Cochrane Colloquium. Edinburgh; 2018.
O'Keefe, H. SCRPT: SCReening Prioritisation Tool. Available from: https://myoutreach.shinyapps.io/SCRPT
Adam GP, Paynter R. Development of literature search strategies for evidence syntheses: pros and cons of incorporating text mining tools and objective approaches. BMJ Evid-Based Med. 2022; 28(2):137-139.
Stockholm Environment Institute. Available from: https://www.sei.org/

Auteurs

Anthea Sutton (A)

Sheffield Centre for Health and Related Research, School of Medicine and Population Health, The University of Sheffield, Sheffield, UK.

Hannah O'Keefe (H)

NIHR Innovation Observatory, Newcastle University, Newcastle, UK.

Eugenie Evelynne Johnson (EE)

NIHR Innovation Observatory, Newcastle University, Newcastle, UK.
Population Health Sciences Institute, Newcastle University, Newcastle, UK.

Christopher Marshall (C)

York Health Economics Consortium, University of York, York, UK.

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Classifications MeSH