Evidence map›Paper›PMID 31296265›Full record

ArticleSystematic reviews2019

Toward systematic review automation: a practical guide to using machine learning tools in research synthesis.

Iain J Marshall, Byron C Wallace

Abstract readEditorial
PubMed Full text at PMC Publisher
In one paragraph

Article in Systematic reviews, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 203 papers, 30 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
203citing papers in PubMed, 30 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

203 citing papers in PubMed, 30 syntheses or guidelines pooled it.

  1. Hearing aids for mild to moderate hearing loss in adults.The Cochrane database of systematic reviews · 2026
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  18. The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis.Perspectives on psychological science : a journal of the Association for Psychological Science · 2022
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143 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

2 authors.

Iain J MarshallSchool of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 3rd Floor, Addison House, Guy's Campus, London, SE1 1UL, UK. iain.marshall@kcl.ac.uk.ORCID 0000-0003-2594-2654
Byron C WallaceKhoury College of Computer Sciences, Northeastern University, 202 WVH, 360 Huntington Avenue, Boston, MA, 02115, USA.

Funding

Semi-Automating Data Extraction for Systematic ReviewsR01LM012086 · NLM · UNIVERSITY OF TEXAS AT AUSTIN · PI MARSHALL, IAIN, WALLACE, BYRON CASEY · 2015 to 2022
$2.4M
Medical Research Council MR/N015185/1NLM NIH HHS R01 LM012086U.S. National Library of Medicine R01-LM012086-01A1
6 · The paper itself

Abstract

Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice.

Indexed as

Practice Guidelines as TopicSystematic Reviews as TopicAutomationEvidence-Based MedicineHumansMachine LearningEvidence synthesisMachine learningNatural language processing

Identifiers

PMID31296265
PMCPMC6621996

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.