Evidence map›Paper›PMID 20102628›Full record

ArticleBMC bioinformatics2010

Semi-automated screening of biomedical citations for systematic reviews.

Byron C Wallace, Thomas A Trikalinos, Joseph Lau, Carla Brodley, Christopher H Schmid

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 17 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
116citing papers in PubMed, 17 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

116 citing papers in PubMed, 17 syntheses or guidelines pooled it.

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  16. Adverse events following implantable cardioverter defibrillator implantation: a systematic review.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2014
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56 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

5 authors.

Byron C WallaceDepartment of Computer Science, Tufts University, Medford, MA, USA. bwallace@tuftsmedicalcenter.org
Thomas A Trikalinos
Joseph Lau
Carla Brodley
Christopher H Schmid

Funding

A System for Comprehensive Meta-AnalysisR33RR017109 · NCRR · TUFTS MEDICAL CENTER · PI SCHMID, CHRISTOPHER H. · 2002 to 2004
$1.2M
Semi-Automated Abstract Screening for Comparative Effectiveness ReviewsR01HS018494 · AHRQ · TUFTS MEDICAL CENTER · PI TRIKALINOS, THOMAS · 2009 to 2011
$1.2M
AHRQ HHS R01 HS018494NCRR NIH HHS R33 RR17109
6 · The paper itself

Abstract

backgroundSystematic reviews address a specific clinical question by unbiasedly assessing and analyzing the pertinent literature. Citation screening is a time-consuming and critical step in systematic reviews. Typically, reviewers must evaluate thousands of citations to identify articles eligible for a given review. We explore the application of machine learning techniques to semi-automate citation screening, thereby reducing the reviewers' workload.

resultsWe present a novel online classification strategy for citation screening to automatically discriminate "relevant" from "irrelevant" citations. We use an ensemble of Support Vector Machines (SVMs) built over different feature-spaces (e.g., abstract and title text), and trained interactively by the reviewer(s). Semi-automating the citation screening process is difficult because any such strategy must identify all citations eligible for the systematic review. This requirement is made harder still due to class imbalance; there are far fewer "relevant" than "irrelevant" citations for any given systematic review. To address these challenges we employ a custom active-learning strategy developed specifically for imbalanced datasets. Further, we introduce a novel undersampling technique. We provide experimental results over three real-world systematic review datasets, and demonstrate that our algorithm is able to reduce the number of citations that must be screened manually by nearly half in two of these, and by around 40% in the third, without excluding any of the citations eligible for the systematic review.

conclusionsWe have developed a semi-automated citation screening algorithm for systematic reviews that has the potential to substantially reduce the number of citations reviewers have to manually screen, without compromising the quality and comprehensiveness of the review.

Indexed as

Review Literature as TopicInformation Storage and RetrievalPeriodicals as TopicPublications

Identifiers

PMID20102628
PMCPMC2824679

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.