Evidence map›Paper›PMID 39175822›Full record

ArticleFrontiers in medicine2024

Prediction of adenomyosis according to revised definitions of morphological uterus sonographic assessment features.

Onur Yavuz, Asli Akdöner, Mehmet Eyüphan Özgozen, Begüm Ertan, Sefa Kurt, Emine Cagnur Ulukuş, Mehmet Güney

Abstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Treatment selection for adenomyosis based on imaging findings and patient characteristics.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
    Review
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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

7 authors.

Onur YavuzDepartment of Obstetrics and Gynecology, Dokuz Eylül University School of Medicine, Izmir, Türkiye.
Asli AkdönerDepartment of Obstetrics and Gynecology, Dokuz Eylül University School of Medicine, Izmir, Türkiye.
Mehmet Eyüphan ÖzgozenDepartment of Obstetrics and Gynecology, Dokuz Eylül University School of Medicine, Izmir, Türkiye.
Begüm ErtanDepartment of Obstetrics and Gynecology, Dokuz Eylül University School of Medicine, Izmir, Türkiye.
Sefa KurtDepartment of Obstetrics and Gynecology, Dokuz Eylül University School of Medicine, Izmir, Türkiye.
Emine Cagnur UlukuşDepartment of Pathology, Dokuz Eylül University School of Medicine, Izmir, Türkiye.
Mehmet GüneyDepartment of Obstetrics and Gynecology, Dokuz Eylül University School of Medicine, Izmir, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aimed to predict the diagnosis of adenomyosis by revised definitions of morphological uterus sonographic assessment (MUSA) features in individuals who had hysterectomy. Methods: This was retrospective cohort research conducted at a tertiary facility. Between January 2022 and January 2023, 196 individuals who had hysterectomy were analyzed in the research. The revised definitions of MUSA features of the adenomyosis approach were used to record the direct and indirect results of the sonography. The cases were classified as Group 1 (adenomyosis; Results: Hyperechogenic islands and echogenic subendometrial buds and lines were the most predictive direct features ( Conclusion: This study shows that combinations with a total number of features >4 can be practically used in the evaluation of adenomyosis using the revised definitions of MUSA features.

Indexed as

2D transvaginal ultrasonography3D transvaginal ultrasonographyadenomyosisdirect featureindirect feature

Identifiers

PMID39175822
PMCPMC11338877

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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.