Evidence map›Paper›PMID 29956486›Full record

ArticleResearch synthesis methods2018

Prioritising references for systematic reviews with RobotAnalyst: A user study.

Piotr Przybyła, Austin J Brockmeier, Georgios Kontonatsios, Marie-Annick Le Pogam, John McNaught, Erik von Elm, Kay Nolan, Sophia Ananiadou

Abstract read
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In one paragraph

Article in Research synthesis methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 5 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

54 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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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

8 authors.

Piotr PrzybyłaNational Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0001-9043-6817
Austin J BrockmeierNational Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.
Georgios KontonatsiosNational Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.
Marie-Annick Le PogamCochrane Switzerland, Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland.
John McNaughtNational Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.
Erik von ElmCochrane Switzerland, Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland.
Kay NolanNational Institute for Health and Care Excellence, Manchester, UK.
Sophia AnaniadouNational Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK.

Funding

Medical Research Council MR/L01078X/1Medical Research Council MR/N00583X/1
6 · The paper itself

Abstract

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings.

Indexed as

Data Interpretation, StatisticalMachine LearningReview Literature as TopicSoftwareAlgorithmsCluster AnalysisData MiningHumansReproducibility of ResultsRetrospective StudiesSwitzerland

Identifiers

PMID29956486
PMCPMC6175382

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.