Evidence map›Paper›PMID 27876082›Full record

Trial reportSystematic reviews2016

Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial.

Ian J Saldanha, Christopher H Schmid, Joseph Lau, Kay Dickersin, Jesse A Berlin, Jens Jap, Bryant T Smith, Simona Carini, Wiley Chan, Berry De Bruijn and 7 more

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Systematic reviews, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 11 institutions in 2 countries.

Ian J SaldanhaDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room W6507-B, Baltimore, MD, 21205, USA. isaldan1@jhmi.edu.
Christopher H SchmidDepartment of Biostatistics, and Center for Evidence-based Medicine, Brown University School of Public Health, Providence, RI, USA.
Joseph LauDepartment of Health Services, Policy and Practice, and Center for Evidence-based Medicine, Brown University School of Public Health, Providence, RI, USA.
Kay DickersinDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room W6507-B, Baltimore, MD, 21205, USA.
Jesse A BerlinEpidemiology, Johnson & Johnson, Titusville, NJ, USA.
Jens JapCenter for Evidence-based Medicine, Brown University School of Public Health, Providence, RI, USA.
Bryant T SmithCenter for Evidence-based Medicine, Brown University School of Public Health, Providence, RI, USA.
Simona CariniDepartment of Medicine, University of California San Francisco School of Medicine, San Francisco, CA, USA.
Wiley ChanInternal Medicine, Kaiser Permanente Northwest, Portland, OR, USA.
Berry De BruijnNational Research Council Information and Communications Technologies Portfolio (NRC-ICT), Ottawa, ON, Canada.
Byron C WallaceNortheastern University College of Computer and Information Science, Boston, MA, USA.
Susan M HutflessDepartment of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Ida SimDepartment of Medicine, University of California San Francisco School of Medicine, San Francisco, CA, USA.
M Hassan MuradCollege of Medicine, and Evidence-based Practice Center, Mayo Clinic, ᅟRochester, MN, USA.
Sandra A WalshCalifornia Breast Cancer Organizations, Davis, CA, USA.
Elizabeth J WhamondCochrane Consumer Network, Fredericton, NB, Canada.
Tianjing LiDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room W6507-B, Baltimore, MD, 21205, USA.
Brown University · USJohns Hopkins University · USUniversity of California, San Francisco · USCochrane · CAJohns Hopkins Medicine · USJohnson & Johnson (United States) · USKaiser Permanente · USMayo Clinic · USNational Research Council Canada · CANortheastern University · USZero Breast Cancer · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a novel software application designed to facilitate the abstraction process by allowing users to (1) view study article PDFs juxtaposed to electronic data abstraction forms linked to a data abstraction system, (2) highlight (or "pin") the location of the text in the PDF, and (3) copy relevant text from the PDF into the form. We describe the design of a randomized controlled trial (RCT) that compares the relative effectiveness of (A) DAA-facilitated single abstraction plus verification by a second person, (B) traditional (non-DAA-facilitated) single abstraction plus verification by a second person, and (C) traditional independent dual abstraction plus adjudication to ascertain the accuracy and efficiency of abstraction.

methodsThis is an online, randomized, three-arm, crossover trial. We will enroll 24 pairs of abstractors (i.e., sample size is 48 participants), each pair comprising one less and one more experienced abstractor. Pairs will be randomized to abstract data from six articles, two under each of the three approaches. Abstractors will complete pre-tested data abstraction forms using the Systematic Review Data Repository (SRDR), an online data abstraction system. The primary outcomes are (1) proportion of data items abstracted that constitute an error (compared with an answer key) and (2) total time taken to complete abstraction (by two abstractors in the pair, including verification and/or adjudication). DISCUSSION: The DAA trial uses a practical design to test a novel software application as a tool to help improve the accuracy and efficiency of the data abstraction process during systematic reviews. Findings from the DAA trial will provide much-needed evidence to strengthen current recommendations for data abstraction approaches.

trial registrationThe trial is registered at National Information Center on Health Services Research and Health Care Technology (NICHSR) under Registration # HSRP20152269: https://wwwcf.nlm.nih.gov/hsr_project/view_hsrproj_record.cfm?NLMUNIQUE_ID=20152269&SEARCH_FOR=Tianjing%20Li . All items from the World Health Organization Trial Registration Data Set are covered at various locations in this protocol. Protocol version and date: This is version 2.0 of the protocol, dated September 6, 2016. As needed, we will communicate any protocol amendments to the Institutional Review Boards (IRBs) of Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University. We also will make appropriate as-needed modifications to the NICHSR website in a timely fashion.

Indexed as

Abstracting and IndexingSoftwareSystematic Reviews as TopicEvidence-Based MedicineHumansData abstractionRandomized controlled trialSystematic reviews

Identifiers

PMID27876082
PMCPMC5120497
OpenAlexW2554505213

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.