Evidence map›Paper›PMID 38845913›Full record

ArticleHeliyon2024

Research hotspots and trends in biological agents for psoriasis: Visualization and bibliometric analysis.

Si-Yu Long, Lin Shang, Siqi Zhao, Huijuan Shi, Yan-Ling He

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

5 authors.

Si-Yu LongDepartment of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, China.
Lin ShangDepartment of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, China.
Siqi ZhaoDepartment of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, China.
Huijuan ShiDepartment of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, China.
Yan-Ling HeDepartment of Dermatology, Beijing Chao-Yang Hospital, Capital Medical University, National Clinical Research Center for Skin and Immune Diseases, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Psoriasis is a global health concern, and biological therapies have proven to be highly effective in treating psoriatic patients in many countries. We performed a bibliometric analysis of current research on biological agents for the treatments of psoriasis, investigating research patterns and public interest in this area. We conducted a thorough review of articles on biological agents for psoriasis in the Web of Science Core Collection spanning from 2000 to 2022. Our study involved examining the distribution of these articles based on publication year, affiliations, countries, authors, and journals. To visualize this data effectively, we employed bibliometric tools like CiteSpace and the R package bibliometrix. Our analysis encompassed 8,047 publications. The number of papers published sharply increased from 2009, either reaching its peak in 2022 or not yet reaching it. The United States (n = 2,292), Kristian Reich (n = 166), and

Indexed as

BibliometricBiological agentDermatologyPsoriasisResearch trendsSafety

Identifiers

PMID38845913
PMCPMC11153111

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.