Evidence map›Paper›PMID 37954610›Full record

SynthesisFrontiers in immunology2023

A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis.

Chenyang Yu, Yingzhao Huang, Wei Yan, Xian Jiang

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. 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

4 authors.

Chenyang YuDepartment of Dermatology, West China Hospital, Sichuan University, Chengdu, China.
Yingzhao HuangDepartment of Thoracic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Wei YanDepartment of Dermatology, West China Hospital, Sichuan University, Chengdu, China.
Xian JiangDepartment of Dermatology, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The surge in the number of publications on psoriasis has posed significant challenges for researchers in effectively managing the vast amount of information. However, due to the lack of tools to process metadata, no comprehensive bibliometric analysis has been conducted. Objectives: This study is to evaluate the trends and current hotspots of psoriatic research from a macroscopic perspective through a bibliometric analysis assisted by machine learning based semantic analysis. Methods: Publications indexed under the Medical Subject Headings (MeSH) term "Psoriasis" from 2003 to 2022 were extracted from PubMed. The generative statistical algorithm latent Dirichlet allocation (LDA) was applied to identify specific topics and trends based on abstracts. The unsupervised Louvain algorithm was used to establish a network identifying relationships between topics. Results: A total of 28,178 publications were identified. The publications were derived from 176 countries, with United States, China, and Italy being the top three countries. For the term "psoriasis", 9,183 MeSH terms appeared 337,545 times. Among them, MeSH term "Severity of illness index", "Treatment outcome", "Dermatologic agents" occur most frequently. A total of 21,928 publications were included in LDA algorithm, which identified three main areas and 50 branched topics, with "Molecular pathogenesis", "Clinical trials", and "Skin inflammation" being the most increased topics. LDA networks identified "Skin inflammation" was tightly associated with "Molecular pathogenesis" and "Biological agents". "Nail psoriasis" and "Epidemiological study" have presented as new research hotspots, and attention on topics of comorbidities, including "Cardiovascular comorbidities", "Psoriatic arthritis", "Obesity" and "Psychological disorders" have increased gradually. Conclusions: Research on psoriasis is flourishing, with molecular pathogenesis, skin inflammation, and clinical trials being the current hotspots. The strong association between skin inflammation and biologic agents indicated the effective translation between basic research and clinical application in psoriasis. Besides, nail psoriasis, epidemiological study and comorbidities of psoriasis also draw increased attention.

Indexed as

Arthritis, PsoriaticDermatitisPsoriasisBibliometricsHumansInflammationMachine LearningUnited Statesbibliometriclatent Dirichlet allocation (LDA) algorithmmachine learningnatural language processing (NLP)psoriasis

Identifiers

PMID37954610
PMCPMC10637956

What OpenQuestion holds

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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.