A visualization analysis of global research trends in targeted therapies for thyroid carcinoma (2013–2023)

Medicine, Journal Year: 2025, Volume and Issue: 104(11), P. e41835 - e41835

Published: March 14, 2025

This study aims to analyze and identify primary research trends in targeted therapy for thyroid carcinoma (TC). It seeks provide a factual foundation researchers, as TC often presents with advanced stages aggressive subtypes, leading unfavorable clinical outcomes. The evolution of therapies introduces promising treatment possibilities, necessitating bibliometric analysis better understand the current state this field. A comprehensive was conducted using data from Web Science Core Collection (WOSCC). Advanced search queries established literature database, performed tools such VOSviewer, CiteSpace, Tableau, Microsoft Excel. focused on publications 2013 2023, examining patterns, geographical contributions, institutional output, influential journals. identified 763 during period, significant contributions United States, China, Italy, States output. Research activity peaked 2021, showing overall fluctuating growth. Key contributing institutions included University Texas MD Anderson Cancer Center Pisa. Notable journals, Cancers Thyroid, were among most cited, underscoring their impact highlighted an increase global output robust international collaborations, particularly countries. provides overview TC. identifies key development processes hotspots, offering valuable insights guide future directions. findings aim stimulate further studies foster advancements critical area oncology.

Language: Английский

A Comprehensive Bibliometric Analysis and Visualization of Publication Trends in Hip-Spine Syndrome

World Neurosurgery, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Bibliometric analysis.

Language: Английский

Global trends in machine learning applied to clinical research in liver cancer: Bibliometric and visualization analysis (2001–2024)

Medicine, Journal Year: 2024, Volume and Issue: 103(49), P. e40790 - e40790

Published: Dec. 6, 2024

This study explores the intersection of liver cancer and machine learning through bibliometric analysis. The aim is to identify highly cited papers in field examine current research landscape, highlighting emerging trends key areas focus learning. By analyzing citation patterns, this sheds light on evolving role its potential for future advancements.

Language: Английский