Evidence map›Paper›PMID 38654734›Full record

ArticleFrontiers in neurology2024

Machine learning techniques based on

Kai Liao, Huanhua Wu, Yuanfang Jiang, Chenchen Dong, Hailing Zhou, Biao Wu, Yongjin Tang, Jian Gong, Weijian Ye, Youzhu Hu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 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. Article
  2. Article
  3. Article
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

12 authors.

Kai Liao *Department of Nuclear Medicine and PET/CT-MRI Center, Institute of Molecular and Functional Imaging, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Huanhua Wu *The Affiliated Shunde Hospital of Jinan University, Foshan, Guangdong, China.
Yuanfang JiangDepartment of Nuclear Medicine and PET/CT-MRI Center, Institute of Molecular and Functional Imaging, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Chenchen DongDepartment of Nuclear Medicine and PET/CT-MRI Center, Institute of Molecular and Functional Imaging, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Hailing ZhouDepartment of Radiology, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.
Biao WuDepartment of Nuclear Medicine and PET/CT-MRI Center, Institute of Molecular and Functional Imaging, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Yongjin TangDepartment of Nuclear Medicine and PET/CT-MRI Center, Institute of Molecular and Functional Imaging, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Jian GongDepartment of Nuclear Medicine and PET/CT-MRI Center, Institute of Molecular and Functional Imaging, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Weijian YeDepartment of Nuclear Medicine and PET/CT-MRI Center, Institute of Molecular and Functional Imaging, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Youzhu HuThe Affiliated Shunde Hospital of Jinan University, Foshan, Guangdong, China.
Qiang GuoEpilepsy Center, Guangdong 999 Brain Hospital, Affiliated Brain Hospital of Jinan University, Guangzhou, Guangdong, China.
Hao XuDepartment of Nuclear Medicine and PET/CT-MRI Center, Institute of Molecular and Functional Imaging, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aimed to investigate the clinical application of Methods: A total of 347 subjects who underwent Results: The final radiomics features ( Conclusion: The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.

Indexed as

18F-FDGmachine learningpositron emission tomography (PET)radiomicstemporal lobe epilepsy

Identifiers

PMID38654734
PMCPMC11035742

What OpenQuestion holds

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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.