Evidence map›Paper›PMID 42798114›Full record

ArticleMedicine2026

1000 highly cited articles in depression research (2016-2026): A bibliometric and scoping review (global depression research: 2016-2026).

Jinlei Li, Fen Ai, Yu Li, Bingxin Cheng, Zhen Chen

Abstract readScoping Review
In one paragraph

Article in Medicine, 2026. 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.

Jinlei LiDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0009-0002-1549-1378
Fen AiDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yu LiDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Bingxin ChengDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zhen ChenDepartment of Emergency Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression is one of the world's leading causes of disability, creating significant challenges for individuals and society. Although research in this field has grown rapidly, we still lack a complete understanding of how depression research has evolved globally over the past decade, especially regarding the long-term effects of the Coronavirus disease 2019pandemic.

methodsWe collected data from the Web of Science Core Collection as of April 28, 2026. After careful screening, we included 1000 highly cited articles. Using bibliometric methods and visualization tools, we analyzed collaboration networks, keyword patterns, emerging topics, and timeline developments.

resultsOur analysis shows that the United States produces more than half of the world's high-impact depression research. Research focus has shifted from traditional theories to new areas like brain-immune connections and gut bacteria, leading to comprehensive treatment approaches that combine digital technology. The Coronavirus disease 2019 pandemic served as a major turning point, significantly reshaping research priorities and accelerating the integration of medicine and data science.

conclusionsOver the past decade, depression research has dynamically evolved in response to external challenges. Exploring basic biological mechanisms and combining knowledge from different fields have become central trends. Our comprehensive analysis provides valuable insights for planning future research, developing policies, and understanding the direction toward "multimodal integration" in depression treatment.

Indexed as

BibliometricsBiomedical ResearchDepressionCOVID-19HumansPandemicsSARS-CoV-2bibliometricsdepressionmajor depressive disorderscoping reviewvisual analysis

Identifiers

PMID42798114
PMCPMC13619249

What OpenQuestion holds

Textmetadata
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.