Evidence map›Paper›PMID 33795003›Full record

ArticleSystematic reviews2021

Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews.

Kevin E K Chai, Robin L J Lines, Daniel F Gucciardi, Leo Ng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Systematic reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 19 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 19 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

71 citing papers in PubMed, 19 syntheses or guidelines pooled it.

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11 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.

Kevin E K ChaiCurtin Institute for Computation, Curtin University, Perth, Australia.
Robin L J LinesSchool of Allied Health, Curtin University, Perth, Australia.ORCID 0000-0003-3687-8870
Daniel F GucciardiSchool of Allied Health, Curtin University, Perth, Australia.
Leo NgSchool of Allied Health, Curtin University, Perth, Australia. leo.ng@curtin.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSystematic reviews and meta-analyses provide the highest level of evidence to help inform policy and practice, yet their rigorous nature is associated with significant time and economic demands. The screening of titles and abstracts is the most time consuming part of the review process with analysts required review thousands of articles manually, taking on average 33 days. New technologies aimed at streamlining the screening process have provided initial promising findings, yet there are limitations with current approaches and barriers to the widespread use of these tools. In this paper, we introduce and report initial evidence on the utility of Research Screener, a semi-automated machine learning tool to facilitate abstract screening.

methodsThree sets of analyses (simulation, interactive and sensitivity) were conducted to provide evidence of the utility of the tool through both simulated and real-world examples.

resultsResearch Screener delivered a workload saving of between 60 and 96% across nine systematic reviews and two scoping reviews. Findings from the real-world interactive analysis demonstrated a time saving of 12.53 days compared to the manual screening, which equates to a financial saving of USD 2444. Conservatively, our results suggest that analysts who scan 50% of the total pool of articles identified via a systematic search are highly likely to have identified 100% of eligible papers.

conclusionsIn light of these findings, Research Screener is able to reduce the burden for researchers wishing to conduct a comprehensive systematic review without reducing the scientific rigour for which they strive to achieve.

Indexed as

Machine LearningMass ScreeningHumansResearchSystematic Reviews as TopicWorkloadAbstract screeningAutomationMachine learningSystematic reviews

Identifiers

PMID33795003
PMCPMC8017894

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.