SynthesisFrontiers in medicine2025
Visual analysis of quality management in Chinese drug clinical trials based on CiteSpace and COOC.
Synthesis in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Recently, considerable progress has been made in the quality of clinical trials conducted in China. However, the number of clinical trials conducted in China still falls below the global average standard. This study aims to identify research hotspots, collaborative networks, and evolutionary trends in the field of clinical trial quality management (CTQM) in China through bibliometrics and visual analyses to provide theoretical support and practical references for the optimization of domestipolicies. Methods: A systematic literature search was performed across the CNKI, Wanfang, and VIP databases to clinical trial quality management CTQM-related publications. Bibliometric analysis was conducted using CiteSpace 6.1.R6 and Co-Occurrence 20.5 (COOC 20.5), with key metrics including: annual output, active institutions, core journals, main authors, keywords, and thematic evolution. To capture internationally published works, supplementary searches were executed in Scopus, Web of Science, and PubMed for CTQM publications authored by Chinese scholars. Owing to the limited number of results (6 records), these documents were only included only in the discussion analysis. Results: A total of 528 articles were retrieved from the field of CTQM. The research process was divided into three periods: the basic standardization period (2003-2012), technology convergence period (2013-2019), and the intelligent transformation period (2020-2024). The theme shifted from the localization of the system to risk management, data management, and ethical governance driven by emerging technologies. The issuing organizations are primarily national-level administrative bodies, showing strong political-academic collaboration but limited cross-system partnerships. Artificial intelligence (AI)-based clinical trial quality management enhances quality control (QC) efficiency; however, it raises concerns about data privacy and ethical disparities. Conclusion: China's research in the field of CTQM has led to the innovative integration of traditional quality control methods with new technologies. However, insufficient interdisciplinary cooperation and the absence of a data governance system pose ongoing challenges. In the future, it is necessary to build a three-dimensional ecosystem of "policy guidance, technological breakthroughs, and ethical synergy" to promote the rapid development of drug research in China.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.