Evidence map›Paper›PMID 38435550›Full record

ArticlePeerJ. Computer science2024

SEU

Lizhuang Liu, Kun Wu, Ke Wang, Zhenqi Han, Jianxing Qiu, Qiao Zhan, Tian Wu, Jinghang Xu, Zheng Zeng

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
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

9 authors.

Lizhuang LiuShanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.
Kun WuShanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.
Ke WangRadiology Department, Peking University First Hospital, Beijing, China.
Zhenqi HanShanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.
Jianxing QiuRadiology Department, Peking University First Hospital, Beijing, China.
Qiao ZhanDepartment of Infectious Diseases, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Tian WuDepartment of Infectious Diseases, Peking University First Hospital, Beijing, China.
Jinghang XuDepartment of Infectious Diseases, Peking University First Hospital, Beijing, China.
Zheng ZengDepartment of Infectious Diseases, Peking University First Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver occupying lesions can profoundly impact an individual's health and well-being. To assist physicians in the diagnosis and treatment of abnormal areas in the liver, we propose a novel network named SEU

Indexed as

CTDeep learningLiver occupying lesion segmentationSE attentionU2-Net

Identifiers

PMID38435550
PMCPMC10909188

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.