Evidence map›Paper›PMID 30997295›Full record

ArticlePeerJ2019

Crowdsourcing in medical research: concepts and applications.

Joseph D Tucker, Suzanne Day, Weiming Tang, Barry Bayus

Abstract read
In one paragraph

Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 1 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

81 citing papers in PubMed, 1 synthesis or guideline pooled it.

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21 more citing papers are in PubMed but not listed here.

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

4 authors.

Joseph D TuckerInstitute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Suzanne DayInstitute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Weiming TangInstitute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Barry BayusKenan-Flagler School of Business, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

Virology, Immunology, and Microbiology CoreP30AI050410 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVID M. MARGOLIS · 2001 to 2026
$76.9M
North Carolina Translational & Clinical Sciences Institute (NC TraCS)UL1TR001111 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, CAREY, TIMOTHY S · 2013 to 2017
$43.9M
Spurring Innovation in HIV Testing and Linkage: A Crowdsourcing Approach -Diversity SupplementR01AI114310 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI TUCKER, JOSEPH DAVID, WEI, CHONGYI · 2014 to 2018
$3.3M
ITEST: Innovative Tools to Expand Youth-Friendly HIV Self-TestingUG3HD096929 · NICHD · SAINT LOUIS UNIVERSITY · PI EZECHI, OLIVER CHUKWUJEKWU, IWELUNMOR, JULIET · 2018 to 2019
$2.3M
Mentoring Multidisciplinary Global HIV Patient-Oriented ResearchK24AI143471 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Joseph David Tucker · 2019 to 2026
$1.4M
NCATS NIH HHS UL1 TR001111NIAID NIH HHS K24 AI143471NIAID NIH HHS P30 AI050410NIAID NIH HHS R01 AI114310NICHD NIH HHS UG3 HD096929
6 · The paper itself

Abstract

Crowdsourcing shifts medical research from a closed environment to an open collaboration between the public and researchers. We define crowdsourcing as an approach to problem solving which involves an organization having a large group attempt to solve a problem or part of a problem, then sharing solutions. Crowdsourcing allows large groups of individuals to participate in medical research through innovation challenges, hackathons, and related activities. The purpose of this literature review is to examine the definition, concepts, and applications of crowdsourcing in medicine. This multi-disciplinary review defines crowdsourcing for medicine, identifies conceptual antecedents (collective intelligence and open source models), and explores implications of the approach. Several critiques of crowdsourcing are also examined. Although several crowdsourcing definitions exist, there are two essential elements: (1) having a large group of individuals, including those with skills and those without skills, propose potential solutions; (2) sharing solutions through implementation or open access materials. The public can be a central force in contributing to formative, pre-clinical, and clinical research. A growing evidence base suggests that crowdsourcing in medicine can result in high-quality outcomes, broad community engagement, and more open science.

Indexed as

Collective intelligenceCrowdsourcingLiterature reviewOpen source modelTheory

Identifiers

PMID30997295
PMCPMC6463854

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.