Evidence map›Paper›PMID 29950851›Full record

ArticleTherapeutics and clinical risk management2018

Gynecological emergency ultrasound in daytime and at night: differences that cannot be ignored.

Bin-Bin Jin, Yi-Zhen Gong, Yan Ma, Zhong-Hui He

Open access · goldAbstract read
In one paragraph

Article in Therapeutics and clinical risk management, 2018. 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
0.5field-weighted citation impact, top 29% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

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

4 authors at 2 institutions in 2 countries.

Bin-Bin Jin *Department of Ultrasonography.
Yi-Zhen Gong *Department of Evidence-Based Medicine.
Yan MaDepartment of Ultrasonography.
Zhong-Hui HeDepartment of Gynecology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
Evidence Based Research (United States) · USFirst Affiliated Hospital of GuangXi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUltrasonography, the preferred adjunct examination method and the differential diagnostic tool for gynecologic emergency, can reflect the change pattern of gynecological diseases in daytime and at night. The purpose of this study was to analyze the diseases through gynecological emergency ultrasound in daytime and at night and to evaluate the day-night difference in the accuracy of ultrasound diagnosis. MATERIALS AND

methodsRetrospective analysis was performed on the 2016 clinical data of the patients who had undergone gynecological emergency ultrasound at The First Affiliated Hospital of Guangxi Medical University, and the results of the ultrasound diagnosis were compared with the clinical diagnosis.

resultsThe following categories of gynecologic emergency diseases during the daytime and at night have significant statistical differences in the constituent ratio (

conclusionSince the different constituent ratio of disease between daytime and night gynecological emergency ultrasound was significantly different, and the diagnostic accuracy of ultrasound at night was low; hence, the ultrasound doctor at night should pay attention to improve the accuracy of diagnosis and the ability of differential diagnosis of ectopic pregnancy, and it is suggested that some suspected cases during the night should be reexamined again in the daytime to eliminate the errors resulted from subjective factors.

Indexed as

daytimedifferenceemergencygynecologynightultrasound

Identifiers

PMID29950851
PMCPMC6016594
OpenAlexW2808852306

What OpenQuestion holds

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Registered trials

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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.