Evidence map›Paper›PMID 35463685›Full record

ArticleJournal of healthcare engineering2022

Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model.

Bo Duan, Zhengmin Xu, Lili Pan, Wenxia Chen, Zhongwei Qiao

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 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 It has been retracted, and should not be counted.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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. [Wideband acoustic immittance characteristics and machine learning-based diagnostic model for children with large vestibular aqueduct syndrome].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024
    Article
  4. Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023
    Review
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Bo DuanDepartment of Otolaryngology-Head and Neck Surgery, Children's Hospital of Fudan University, Shanghai 201102, China.ORCID 0000-0003-3104-9037
Zhengmin XuDepartment of Otolaryngology-Head and Neck Surgery, Children's Hospital of Fudan University, Shanghai 201102, China.
Lili PanDepartment of Radiology, Children's Hospital of Fudan University, Shanghai 201102, China.
Wenxia ChenDepartment of Otolaryngology-Head and Neck Surgery, Children's Hospital of Fudan University, Shanghai 201102, China.ORCID 0000-0002-6942-0666
Zhongwei QiaoDepartment of Radiology, Children's Hospital of Fudan University, Shanghai 201102, China.ORCID 0000-0001-5431-7079
Children's Hospital of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervention. From January 2001 to January 2016, patients with hearing impairment diagnosed as LVAS in infancy in the Department of Otorhinolaryngology, Head and Neck Surgery, Children's Hospital of Fudan University were collected and divided into the stable HL group (

Indexed as

Deep LearningVestibular AqueductVestibular DiseasesChildHearingHumansPrognosisRetrospective StudiesSyndrome

Identifiers

PMID35463685
PMCPMC9020928
OpenAlexW4224059116

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.