Evidence map›Paper›PMID 39678058›Full record

ArticleNoro psikiyatri arsivi2024

Clinical Neurology Research Output From Türkiye: A Bibliometric Analysis.

Seden Demirci, Fatma Yardibi

Abstract read
In one paragraph

Article in Noro psikiyatri arsivi, 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. Review
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

2 authors.

Seden DemirciAkdeniz University School of Medicine, Department of Neurology, Antalya, Türkiye.
Fatma YardibiPostdoctoral Resercher, Akdeniz University, Departmant of Electrical Electronic Engineering, Antalya, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Bibliometric analysis is a comprehensive method used to investigate the academic impact and characteristics of publications in a research field. It has been widely used in various fields of medicine. However, bibliometric analysis of publications in the field of clinical neurology from Türkiye has not yet been evaluated. In this study, we aimed to analyze the publications in the field of clinical neurology in the last decade from Türkiye using the bibliometric method and network analysis and to determine research trends and key issues. Methods: We carried out a search of the Web of Science Core Collection database for articles. CiteSpace Advances 6.1. R 1 and VOSviewer (version 1.6.9) were used for bibliometric analyses and visualization. Results: The research data consisted of 8404 articles in the category of the clinical neurology from Türkiye between 2012 and 2022. The number of publications rose over time, with a mean annual growth rate of 6%. From the document co-citation analysis, chronic migraine and mental health clusters were active current areas. From the keywords analysis, endovascular treatment, depression, oxidative stress, Parkinson's disease, epilepsy, multiple sclerosis, and ischemic stroke were active clusters. Analysis of the authors showed that the most active authors were Betul Baykan, Cavit Boz, and Erdem Tuzun, and MS was the most active topic in this area and recent trend. Conclusions: Clinical neurology is a rapidly expanding research area and attracting more and more attention from the authors. Our study can provide researchers who study the clinical neurology with valuable information on the current status and trends in this field and to facilitate for future studies.

Indexed as

Bibliometric analysiscitespaceclinical neurologyresearch outputTürkiyeVOSviewer

Identifiers

PMID39678058
PMCPMC11638577

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.