Evidence map›Paper›PMID 37685788›Full record

ReviewJournal of clinical medicine2023

A History of COVID-19 in Pregnancy: A Narrative Review.

Shahrukh Chaudhry, Omar Aboudawoud, Ghislain Hardy

Open access · goldAbstract readReview
In one paragraph

Review in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% of its field
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

3 authors at 1 institution in 1 country.

Shahrukh ChaudhryPaul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX 79905, USA.ORCID 0009-0001-0878-1281
Omar AboudawoudPaul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX 79905, USA.
Ghislain HardyDepartment of Obstetrics and Gynecology, Texas Tech University Health Sciences Center El Paso, El Paso, TX 79905, USA.
Texas Tech University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread across the world causing a global pandemic. During a pandemic, it becomes increasing important to evaluate the effects on specific populations at risk. In this narrative review, we analyzed the literature regarding COVID-19 infection on the pregnant population as they are at increased risk of infection. COVID-19 did seem to significantly increase the risk of obstetric complications, specifically in underserved and marginalized populations. In general, COVID-19 rarely directly infected the fetus and placenta, apart from a very rare complication called COVID placentitis. In actuality, the mothers were at greatest direct risk due to COVID-19 infection. The most important takeaway from this pandemic is the prospective lesson and effect it had on social determinants of health. Women did not have safe access to antenatal care, leading to a plethora of indirect obstetric complications due to COVID-19. In conclusion, it was women who suffered from the pandemic, not the placenta nor the fetus. It is our duty as physicians to protect pregnant women, allowing the placenta to protect the fetus.

Indexed as

COVID-19COVID placentitismaternal healthpregnancySARS-CoV-2

Identifiers

PMID37685788
PMCPMC10488531
OpenAlexW4386369321

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.