ArticleTobacco induced diseases2022
Bibliometric analysis of traditional Chinese medicine for smoking cessation.
Article in Tobacco induced diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Assessment of the reliability and quality of pancreatic cancer related short videos on mainstream platforms: cross-sectional study.BMC cancer · 2025Article
- Uncovering the gut - skin axis: the role of specific traditional Chinese medicine interventions in regulating gut microbiota for diabetic foot ulcers and the analysis of research status.Frontiers in pharmacology · 2025Review
- Comparative analysis of NAFLD-related health videos on TikTok: a cross-language study in the USA and China.BMC public health · 2024Article
- Research on Chinese medicinal materials cultivation: A bibliometric and visual analysis.Heliyon · 2024Article
- Bibliometric analysis of RNA vaccines for cancer.Human vaccines & immunotherapeutics · 2023Article
- Traditional Chinese medicine for smoking cessation: An umbrella review of systematic reviews and meta-analysis of randomized controlled trials.Tobacco induced diseases · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSmoking cessation is an efficient approach to reducing disease burden. Traditional Chinese Medicine (TCM) therapies such as acupuncture, acupressure, and herbal drugs are often used to help quit smoking. However, there is a lack of overarching bibliometric analysis of the clinical research on smoking cessation focusing on TCM. The aim of our study is to explore the current patterns and trends of TCM therapy for smoking cessation through bibliometric methods with visual presentation.
methodsThis study is an assessment of academic publications retrieved from the Scopus database on smoking cessation using TCM therapy published in the period 2005-2021. Sankey diagram, word-cloud, network analysis, thematic maps, tree-maps, and the collaborative work of authors, institutions and countries, were used to identify research trends on TCM therapy for smoking cessation. The total cited index and H-index (for journals, authors, countries, organizations) were used to identify the trends of worldwide development by R Package and Excel 2016.
resultsThere was an upward trend, with some fluctuations, of 1908 articles from 2005 to 2021. The most productive country was China. The top institution in this field was Beijing University. The dominant author that contributed to TCM therapy for smoking cessation was Wang Y, who has the highest H-Index. The most productive cited journals were
conclusionsA substantial number of articles on TCM therapy for smoking cessation, mainly focusing on TCM and acupuncture were identified. It is worth noting that research that focused on TCM therapy for smoking cessation also was related to COVID-19.
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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.