Evidence map›Paper›PMID 36284336›Full record

ArticleSystematic reviews2022

Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE.

Ahmed A Al-Jaishi, Monica Taljaard, Melissa D Al-Jaishi, Sheikh S Abdullah, Lehana Thabane, P J Devereaux, Stephanie N Dixon, Amit X Garg

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Systematic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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

5 citing papers in PubMed.

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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.

Ahmed A Al-JaishiLawson Health Research Institute, 800 Commissioners Rd E, London, ON, Canada. Ahmed.AlJaishi@lhsc.on.ca.ORCID 0000-0003-0376-2214
Monica TaljaardClinical Epidemiology Program, School of Epidemiology and Public Health, Ottawa Hospital Research Institute, University of Ottawa, 501 Smyth Road, Ottawa, ON, Canada.
Melissa D Al-JaishiLondon Health Sciences Centre, 800 Commissioners Rd E, London, ON, Canada.
Sheikh S AbdullahDepartment of Computer Science, Western University, 1151 Richmond St, London, ON, Canada.
Lehana ThabaneDepartment of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main St W, Hamilton, ON, Canada.
P J DevereauxDepartment of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main St W, Hamilton, ON, Canada.
Stephanie N DixonLawson Health Research Institute, 800 Commissioners Rd E, London, ON, Canada.
Amit X GargLawson Health Research Institute, 800 Commissioners Rd E, London, ON, Canada.

Funding

CIHR MYG-151209
6 · The paper itself

Abstract

backgroundCluster randomized trials (CRTs) are becoming an increasingly important design. However, authors of CRTs do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making retrieval from bibliographic databases difficult. Machine learning algorithms may improve their identification and retrieval. Therefore, we aimed to develop machine learning algorithms that accurately determine whether a bibliographic citation is a CRT report.

methodsWe trained, internally validated, and externally validated two convolutional neural networks and one support vector machine (SVM) algorithm to predict whether a citation is a CRT report or not. We exclusively used the information in an article citation, including the title, abstract, keywords, and subject headings. The algorithms' output was a probability from 0 to 1. We assessed algorithm performance using the area under the receiver operating characteristic (AUC) curves. Each algorithm's performance was evaluated individually and together as an ensemble. We randomly selected 5000 from 87,633 citations to train and internally validate our algorithms. Of the 5000 selected citations, 589 (12%) were confirmed CRT reports. We then externally validated our algorithms on an independent set of 1916 randomized trial citations, with 665 (35%) confirmed CRT reports.

resultsIn internal validation, the ensemble algorithm discriminated best for identifying CRT reports with an AUC of 98.6% (95% confidence interval: 97.8%, 99.4%), sensitivity of 97.7% (94.3%, 100%), and specificity of 85.0% (81.8%, 88.1%). In external validation, the ensemble algorithm had an AUC of 97.8% (97.0%, 98.5%), sensitivity of 97.6% (96.4%, 98.6%), and specificity of 78.2% (75.9%, 80.4%)). All three individual algorithms performed well, but less so than the ensemble.

conclusionsWe successfully developed high-performance algorithms that identified whether a citation was a CRT report with high sensitivity and moderately high specificity. We provide open-source software to facilitate the use of our algorithms in practice.

Indexed as

AlgorithmsMachine LearningHumansMEDLINERandomized Controlled Trials as TopicSubject HeadingsSupport Vector MachineBibliographic databasesCluster randomized controlled trialMachine learningPredictionSensitivity and specificity

Identifiers

PMID36284336
PMCPMC9594883

What OpenQuestion holds

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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.