Evidence map›Paper›PMID 37964218›Full record

ArticleBMC medical imaging2023

Evaluation of the diagnostic utility on 1.5T and 3.0T

Biao Qu, Hejuan Tan, Min Xiao, Dongbao Liu, Shijin Wang, Yiwen Zhang, Runhan Chen, Gaofeng Zheng, Yonggui Yang, Gen Yan and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical imaging, 2023. 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
0.9field-weighted citation impact, top 26% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  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

11 authors at 4 institutions in 1 country.

Biao QuDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China.
Hejuan TanInstitute of Artificial Intelligence, Xiamen University, Xiamen, China.
Min XiaoInstitute of Artificial Intelligence, Xiamen University, Xiamen, China.
Dongbao LiuBiomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen, China.
Shijin WangDepartment of Information & Computational Mathematics, Xiamen University, Xiamen, China.
Yiwen ZhangDepartment of Neurology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China.
Runhan ChenNational Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
Gaofeng ZhengDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China.
Yonggui YangDepartment of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China. yangyonggui125@sina.cn.
Gen YanDepartment of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China. gyan@stu.edu.cn.
Xiaobo QuBiomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen, China. quxiaobo@xmu.edu.cn.
Xiamen University · CNThe Second Affiliated Hospital of Xiamen Medical CollegeXiamen Chang Gung Hospital · CNXiamen University of Technology · CN

Funding

Fujian Provincial Health Young and Middle-aged Key Talents Training Project 2020GGB067General project of Natural Science Foundation of Fujian Province 2022J011384National Natural Science Foundation of China 62122064,61971361Science and Technology Planning Project of Fujian Province 2020H6003
6 · The paper itself

Abstract

background

methodsOur retrospective collections included the single-voxel

resultsThree metabolite ratios (NAA/Cr, NAA/Cho, and NAA/(Cho + Cr) showed the same statistical difference (p < 0.05) in distinguishing the TLE from HCs in the bilateral temporal poles when using 1.5T or 3.0T scanners. Similarly, the power analysis demonstrated that four metabolite ratios (NAA/Cr, NAA/Cho, NAA/(Cho + Cr), Cho/Cr) had similar distinction abilities between 1.5T and 3.0T scanner, denoting both 1.5T and 3.0T scanners were provided with similar sensitivities and reproducibilities for metabolites detection. Moreover, the metabolite ratios of the same healthy volunteers were not statistically different between 1.5T and 3.0T scanners, except for NAA/Cho (p < 0.05).

conclusions1.5T and 3.0T scanners may have comparable diagnostic potential when

Indexed as

Epilepsy, Temporal LobeCholineCreatineHumansMagnetic Resonance ImagingMagnetic Resonance SpectroscopyRetrospective StudiesTemporal LobeCholineCreatine1.5T1H-MRS3.0TBrainMetabolitesTemporal lobe epileptic

Identifiers

PMID37964218
PMCPMC10644657
OpenAlexW4388652843

What OpenQuestion holds

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LicenceCC BY
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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.