Evidence map›Paper›PMID 33222027›Full record

SynthesisMaternal and child health journal2021

Problems with Early Systematic Reviews: The Case of Coronavirus Disease 2019 (COVID-19) in Pregnancy.

Xue Ling Ang, Sonali Prashant Chonkar, Monica Shi Qi Chua, Suzanna Sulaiman, Jill Cheng Sim Lee

Abstract readSystematic Review
In one paragraph

Synthesis in Maternal and child health journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. 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

5 authors.

Xue Ling AngYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Sonali Prashant ChonkarDepartment of Obstetrics and Gynaecology, KK Women's and Children's Hospital, Singapore, Singapore.
Monica Shi Qi ChuaDepartment of Obstetrics and Gynaecology, KK Women's and Children's Hospital, Singapore, Singapore.
Suzanna SulaimanDepartment of Obstetrics and Gynaecology, KK Women's and Children's Hospital, Singapore, Singapore.
Jill Cheng Sim LeeDepartment of Urogynaecology, KK Women's and Children's Hospital, Singapore, Singapore. jill.lee.c.s@singhealth.com.sg.ORCID http://orcid.org/0000-0003-1803-7058

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRapid dissemination of findings regarding the Coronavirus Disease 2019 (COVID-19) and its potential effects on pregnancy is crucial to support understanding and development of recommendations for optimization of obstetrics care. However, much of the current studies published are in the form of case reports or case series which can be prone to biases. Other factors also further complicate attempts to analyze data accurately. Hence, this evaluation hopes to highlight some of these problems and provide suggestions to help clinicians mitigate and make reasonable conclusions when reading the abundant yet limited body of evidence when furthering their research efforts.

methodsStudies regarding COVID-19 and pregnancy were searched on databases such as PubMed, EMBASE, Scopus, the Cochrane Library. Manual search of references of select articles were also undertaken. Apart from summarizing study limitations identified by authors, the characteristics of current literature and systematic reviews were also evaluated to identify potential factors affecting accuracy of subsequent analysis.

resultsFactors such as innate biasness in study design of current literature, duplicate reporting, differing inclusion criteria of systematic reviews, scarce data, inadequate follow-up period and limitations of systematic reviews have been shown to hinder the ability for accurate data extrapolation. DISCUSSION: Unless additional studies are conducted in identified areas of data scarcity and a common list of factors affecting accuracy of data analysis are taken into account when developing recommendations, discrepancies will continue to arise and accurate data analysis and valid systematic reviews will be precluded.

Indexed as

Data AccuracyGuidelines as TopicAdultCOVID-19FemaleHumansMaternal Health ServicesPregnancyPregnancy Complications, InfectiousResearch DesignSARS-CoV-2Systematic Reviews as TopicCOVID-19Novel coronavirus 2019PandemicPregnancySystematic reviews

Identifiers

PMID33222027
PMCPMC7680632

What OpenQuestion holds

Textmetadata
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.