Evidence map›Paper›PMID 36128173›Full record

ArticleContrast media & molecular imaging2022

Role of Posttreatment Nursing Based on Functional Magnetic Resonance Imaging in Breast Cancer Patients with Lymphedema.

Ru Sun, Haihong Li, Xin Wang, Jun Shen, Lingzhi Guo, Wenwen Sun

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 52% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Ru SunDepartment of Breast Surgery, The First People's Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China.
Haihong LiDepartment of Nursing, The First People's Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China.
Xin WangDepartment of Otorhinolaryngology Head and Neck Surgery, The First People's Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China.
Jun ShenDepartment of Breast Surgery, The First People's Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China.
Lingzhi GuoDepartment of Breast Surgery, The First People's Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China.
Wenwen SunDepartment of Breast Surgery, The First People's Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China.ORCID 0000-0002-2073-308X
The First People’s Hospital of Lianyungang · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is the tumor disease with the highest incidence in women, especially lymphedema after treatment, which seriously affects the quality of life of women. In order to improve the nursing quality of breast cancer patients, medical staff uses functional magnetic resonance imaging (fMRI) to intervene in breast cancer patients, which greatly improves the recovery speed of patients. In this paper, functional magnetic resonance imaging based on the image registration method is proposed and applied to the follow-up of patients with breast cancer lymphedema after treatment. The powerful imaging effect allows doctors to timely and accurately judge the condition of the patient's lesions after treatment, which is conducive to nursing care. The experimental results of this paper show that the total number of serious patients in group

Indexed as

Breast NeoplasmsLymphedemaFemaleHumansMagnetic Resonance ImagingQuality of Life

Identifiers

PMID36128173
PMCPMC9470330
OpenAlexW4294844539

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.