Evidence map›Paper›PMID 35516453›Full record

ArticleComputational and mathematical methods in medicine2022

Evaluation of Endocrine and Metabolic Changes in Polycystic Ovary Syndrome by Ultrasonic Imaging Features under an Intelligent Algorithm.

Li Wei, Feng Wu, Jianjun Zhang, Jing Li, Di Yang, Guoying Wen

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 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 It has been retracted, and should not be counted.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
0.4field-weighted citation impact, top 37% 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, 2 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Li WeiDepartment of Endosecretory Metabolic Diseases, Baoji People's Hospital, Baoji, 721000 Shaanxi, China.ORCID https://orcid.org/0000-0001-9932-7561
Feng WuDepartment of Endosecretory Metabolic Diseases, Baoji People's Hospital, Baoji, 721000 Shaanxi, China.ORCID https://orcid.org/0000-0001-6585-9762
Jianjun ZhangDepartment of Endosecretory Metabolic Diseases, Baoji People's Hospital, Baoji, 721000 Shaanxi, China.ORCID https://orcid.org/0000-0002-2724-7076
Jing LiDepartment of Obstetrics and Gynecology, Lanzhou First People's Hospital, Lanzhou, 730030 Gansu, China.ORCID https://orcid.org/0000-0002-9452-1557
Di YangDepartment of Obstetrics and Gynecology, Lanzhou First People's Hospital, Lanzhou, 730030 Gansu, China.ORCID https://orcid.org/0000-0003-3547-0929
Guoying WenDepartment of Obstetrics and Gynecology, Lanzhou First People's Hospital, Lanzhou, 730030 Gansu, China.ORCID https://orcid.org/0000-0001-5372-7690
Baoji City Central Hospital · CNFirst Hospital of Lanzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To explore the application of intelligent algorithm-based ultrasound in the evaluation of polycystic ovary syndrome (PCOS) and the endocrine and metabolic changes of PCOS, 44 patients diagnosed with PCOS were recruited and rolled into three groups regarding detection methods. Backpropagation algorithm-based ultrasonic detection was adopted for the patients in the experimental group. The patients in the control group were tested by conventional ultrasound. In addition, 18 healthy volunteers were selected as the normal group. The results showed that the images processed by the backpropagation algorithm were substantially better than the traditional ultrasound images (

Indexed as

Polycystic Ovary SyndromeAlgorithmsFemaleFollicle Stimulating HormoneHumansUltrasonographyFollicle Stimulating Hormone

Identifiers

PMID35516453
PMCPMC9064505
OpenAlexW4224927305

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.