Literature DB >> 30325115

Features and functioning of Data Abstraction Assistant, a software application for data abstraction during systematic reviews.

Jens Jap1, Ian J Saldanha1, Bryant T Smith1, Joseph Lau1, Christopher H Schmid2, Tianjing Li3.   

Abstract

INTRODUCTION: During systematic reviews, data abstraction is labor- and time-intensive and error-prone. Existing data abstraction systems do not track specific locations and contexts of abstracted information. To address this limitation, we developed a software application, the Data Abstraction Assistant (DAA) and surveyed early users about their experience using DAA. FEATURES OF DAA: We designed DAA to encompass three essential features: (1) a platform for indicating the source of abstracted information, (2) compatibility with a variety of data abstraction systems, and (3) user-friendliness. HOW DAA FUNCTIONS: DAA (1) converts source documents from PDF to HTML format (to enable tracking of source of abstracted information), (2) transmits the HTML to the data abstraction system, and (3) displays the HTML in an area adjacent to the data abstraction form in the data abstraction system. The data abstractor can mark locations on the HTML that DAA associates with items on the data abstraction form. EXPERIENCES OF EARLY USERS OF DAA: When we surveyed 52 early users of DAA, 83% reported that using DAA was either very or somewhat easy; 71% are very or somewhat likely to use DAA in the future; and 87% are very or somewhat likely to recommend that others use DAA in the future. DISCUSSION: DAA, a user-friendly software for linking abstracted data with their exact source, is likely to be a very useful tool in the toolbox of systematic reviewers. DAA facilitates verification of abstracted data and provides an audit trail that is crucial for reproducible research.
© 2018 John Wiley & Sons, Ltd.

Entities:  

Keywords:  data abstraction; data exchange; data tracking; software; systematic reviews

Mesh:

Year:  2018        PMID: 30325115      PMCID: PMC6424629          DOI: 10.1002/jrsm.1326

Source DB:  PubMed          Journal:  Res Synth Methods        ISSN: 1759-2879            Impact factor:   5.273


  15 in total

1.  Single data extraction generated more errors than double data extraction in systematic reviews.

Authors:  Nina Buscemi; Lisa Hartling; Ben Vandermeer; Lisa Tjosvold; Terry P Klassen
Journal:  J Clin Epidemiol       Date:  2006-03-15       Impact factor: 6.437

2.  High prevalence but low impact of data extraction and reporting errors were found in Cochrane systematic reviews.

Authors:  Ashley P Jones; Tracey Remmington; Paula R Williamson; Deborah Ashby; Rosalind L Smyth
Journal:  J Clin Epidemiol       Date:  2005-04-18       Impact factor: 6.437

3.  Data extraction errors in meta-analyses that use standardized mean differences.

Authors:  Peter C Gøtzsche; Asbjørn Hróbjartsson; Katja Maric; Britta Tendal
Journal:  JAMA       Date:  2007-07-25       Impact factor: 56.272

4.  Innovations in data collection, management, and archiving for systematic reviews.

Authors:  Tianjing Li; S Swaroop Vedula; Nira Hadar; Christopher Parkin; Joseph Lau; Kay Dickersin
Journal:  Ann Intern Med       Date:  2015-02-17       Impact factor: 25.391

5.  Systematic review data extraction: cross-sectional study showed that experience did not increase accuracy.

Authors:  Jennifer Horton; Ben Vandermeer; Lisa Hartling; Lisa Tjosvold; Terry P Klassen; Nina Buscemi
Journal:  J Clin Epidemiol       Date:  2009-08-14       Impact factor: 6.437

Review 6.  Automating data extraction in systematic reviews: a systematic review.

Authors:  Siddhartha R Jonnalagadda; Pawan Goyal; Mark D Huffman
Journal:  Syst Rev       Date:  2015-06-15

7.  RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials.

Authors:  Iain J Marshall; Joël Kuiper; Byron C Wallace
Journal:  J Am Med Inform Assoc       Date:  2015-06-22       Impact factor: 4.497

8.  Clinical study reports of randomised controlled trials: an exploratory review of previously confidential industry reports.

Authors:  Peter Doshi; Tom Jefferson
Journal:  BMJ Open       Date:  2013-02-26       Impact factor: 2.692

9.  A Web-based archive of systematic review data.

Authors:  Stanley Ip; Nira Hadar; Sarah Keefe; Christopher Parkin; Ramon Iovin; Ethan M Balk; Joseph Lau
Journal:  Syst Rev       Date:  2012-02-21

10.  A case study of binary outcome data extraction across three systematic reviews of hip arthroplasty: errors and differences of selection.

Authors:  Christopher Carroll; Alison Scope; Eva Kaltenthaler
Journal:  BMC Res Notes       Date:  2013-12-17
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  2 in total

1.  Reproducibility of individual effect sizes in meta-analyses in psychology.

Authors:  Esther Maassen; Marcel A L M van Assen; Michèle B Nuijten; Anton Olsson-Collentine; Jelte M Wicherts
Journal:  PLoS One       Date:  2020-05-27       Impact factor: 3.240

2.  The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research.

Authors:  Ian J Saldanha; Bryant T Smith; Evangelia Ntzani; Jens Jap; Ethan M Balk; Joseph Lau
Journal:  Syst Rev       Date:  2019-12-20
  2 in total

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