Evidence map›Paper›PMID 30325115›Full record

ArticleResearch synthesis methods2019

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

Jens Jap, Ian J Saldanha, Bryant T Smith, Joseph Lau, Christopher H Schmid, Tianjing Li, Data Abstraction Assistant Investigators

Abstract read
In one paragraph

Article in Research synthesis methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. 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

7 authors.

Jens JapCenter for Evidence Synthesis in Health, Department of Health Services, Policy, and Practice, Brown School of Public Health, Providence, Rhode Island.ORCID http://orcid.org/0000-0003-4625-683X
Ian J SaldanhaCenter for Evidence Synthesis in Health, Department of Health Services, Policy, and Practice, Brown School of Public Health, Providence, Rhode Island.
Bryant T SmithCenter for Evidence Synthesis in Health, Department of Health Services, Policy, and Practice, Brown School of Public Health, Providence, Rhode Island.
Joseph LauCenter for Evidence Synthesis in Health, Department of Health Services, Policy, and Practice, Brown School of Public Health, Providence, Rhode Island.
Christopher H SchmidCenter for Evidence Synthesis in Health, Department of Biostatistics, Brown School of Public Health, Providence, Rhode Island.ORCID http://orcid.org/0000-0002-0855-5313
Tianjing LiCenter for Clinical Trials and Evidence Synthesis, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Data Abstraction Assistant Investigators

Funding

Patient-Centered Outcomes Research Institute ME-1310-07009
6 · The paper itself

Abstract

introductionDuring 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.

Indexed as

SoftwareSystematic Reviews as TopicAbstracting and IndexingHumansInternetPattern Recognition, AutomatedProgramming LanguagesReproducibility of ResultsSurveys and QuestionnairesUser-Computer Interfacedata abstractiondata exchangedata trackingsoftwaresystematic reviews

Identifiers

PMID30325115
PMCPMC6424629

What OpenQuestion holds

Textmetadata
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.