Evidence map›Paper›PMID 35463033›Full record

ArticleFrontiers in medicine2022

The Research Landscape of Multiple Endocrine Neoplasia Type 1 (2000-2021): A Bibliometric Analysis.

Chenzhe Feng, Haolin Chen, Leyi Huang, Yeqian Feng, Shi Chang

Abstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Chenzhe FengDepartment of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China.
Haolin ChenDepartment of Mathematics, University of California, Davis, Davis, CA, United States.
Leyi HuangXiangya School of Medicine, Central South University, Changsha, China.
Yeqian FengDepartment of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China.
Shi ChangDepartment of General Surgery, Xiangya Hospital of Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aimed to investigate the landscape of Multiple Endocrine Neoplasia Type 1 research during the last 22 years using machine learning and text analysis. Method: In December 2021, all publications indexed under the MeSH term "Multiple Endocrine Neoplasia Type 1" were obtained from PubMed. The whole set of search results was downloaded in XML format, and metadata such as title, abstract, keywords, mesh words, and year of publication were extracted from the original XML files for bibliometric evaluation. The Latent Dirichlet allocation (LDA) topic modeling method was used to analyze specific themes. Results: This study eventually contained 1,407 publications. Among them, there are 768 (54.58%) case reports and reviews. Text analysis based on MeSH words revealed that the most often studied clinical areas include therapy efficacy, prognosis, and genetic diagnosis. The majority of basic study is focused on genetic alterations. The LDA topic model further identifies three topic clusters include basic research, treatment cluster, and diagnosis cluster. In the basic research cluster, many studies are focused on the expression of Menin. The primary focus of the therapy cluster is pancreatic resections and parathyroidectomy. In the diagnose cluster, the main focus is on Genetic Diagnosis and screening strategies for Hereditary Cancer Syndrome. Conclusion: The current state of research on MEN1 is far from adequate. Research on rare diseases MEN1 necessitates implementing a broad research program involving multiple centers to advance MEN1 research together.

Indexed as

machine learningMultiple Endocrine Neoplasia Type 1natural language processingpublication analysisrare diseases

Identifiers

PMID35463033
PMCPMC9024095

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.