Evidence map›Paper›PMID 39139790›Full record

ArticleFrontiers in medicine2024

A bibliometric analysis of light chain amyloidosis from 2005 to 2024: research trends and hot spots.

Xiangdong Liu, Junyan Wang, Yingmin Li, Weibo Shi, Xiaojing Zhang, Shujin Li, Bin Cong

Abstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Xiangdong Liu *College of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.
Junyan Wang *College of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.
Yingmin LiCollege of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.
Weibo ShiCollege of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.
Xiaojing ZhangCollege of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.
Shujin LiCollege of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.
Bin CongCollege of Forensic Medicine, Hebei Medical University, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Light chain (AL) amyloidosis stands as the most prevalent subtype of systemic amyloidosis, encompassing a group of rare diseases. Here, we evaluated the scientific landscape of AL amyloidosis to investigate research trends and identify hotspots within the field. Methods: Relevant studies on AL amyloidosis published over the past two decades were retrieved from the Web of Science Core Collection. The publications between 2005 and 2024 were subjected to bibliometric analyses, leveraging tools including CiteSpace, VOSviewer, RStudio and MS Excel to analyse and visualize the annual publication trend, co-occurrence patterns, collaborative networks among countries, organizations, and authors. Burst keywords and references were also examined to obtain the research history, and emerging hotspots. Results: The bibliometric analysis included 2,864 articles published between 2005 and 2024. The most productive journal is Conclusion: This bibliometric analysis examined the research developments in AL amyloidosis over the past two decades using bibliometric software. Recent research in this field primarily focuses on two main areas: clinical diagnosis and treatment of AL amyloidosis, as well as cardiac amyloidosis. Emphasis is placed on understanding the mechanisms underlying immunoglobulin light chain aggregation and deposition to mitigate organ involvement.

Indexed as

AL amyloidosisbibliometricslight chain amyloidosisresearch trendWeb of Science

Identifiers

PMID39139790
PMCPMC11320149

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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.