Evidence map›Paper›PMID 35038998›Full record

ReviewBMC pregnancy and childbirth2022

Simpson-Golabi-Behmel syndrome in one of the Dichorionic-diamniotic twin: a case report and literature review.

Yongbing Guo, Huijing Zhang, Lixin Fan, Junya Chen, Xiaoxiao Zhang, Huixia Yang, Yu Sun

Abstract readCase ReportsReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC pregnancy and childbirth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Yongbing Guo *Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, 100034, China.
Huijing Zhang *Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, 100034, China.
Lixin FanDepartment of Obstetrics and Gynecology, Peking University First Hospital, Beijing, 100034, China.
Junya ChenDepartment of Obstetrics and Gynecology, Peking University First Hospital, Beijing, 100034, China.
Xiaoxiao ZhangDepartment of Obstetrics and Gynecology, Peking University First Hospital, Beijing, 100034, China.
Huixia YangDepartment of Obstetrics and Gynecology, Peking University First Hospital, Beijing, 100034, China.
Yu SunDepartment of Obstetrics and Gynecology, Peking University First Hospital, Beijing, 100034, China. sunyu1705@126.com.ORCID http://orcid.org/0000-0001-9303-4207

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSimpson-Golabi-Behmel syndrome (SGBS) is a rare X-linked overgrowth syndrome. The main clinical manifestations are overgrowth and multiple malformations. CASE PRESENTATION: A 38-year-old Chinese woman was pregnant with dichorionic-diamniotic (DCDA) twins after in-vitro fertilization. Series of ultrasound examinations indicated that the measurements (abdominal circumference and estimated foetal weight) of one twin were significantly greater than those of the other one. The genetic testing results of the larger baby indicated of Simpson-Golabi-Behmel syndrome.

conclusionSGBS is difficult to diagnose due to different clinical manifestations. Clinicians need to be more aware of typical SGBS's clinical findings and choose genetic testing methods individually to improve its prenatal diagnosis.

Indexed as

Pregnancy, TwinTwins, DizygoticAdultArrhythmias, CardiacFemaleFrameshift MutationGenetic Diseases, X-LinkedGenetic TestingGigantismGlypicansHeart Defects, CongenitalHumansIntellectual DisabilityPregnancyUltrasonography, PrenatalGlypicansGPC3 protein, humanCase reportDichorionic-diamniotic twinFetal medicineSimpson-Golabi-Behmel syndrome

Identifiers

PMID35038998
PMCPMC8762945

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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.