Evidence map›Paper›PMID 38305810›Full record

ArticleClinical oral investigations2024

Global research trends in tongue cancer from 2000 to 2022: bibliometric and visualized analysis.

Beibei Wu, Tong Zhang, Ning Dai, Ding Luo, Xuejie Wang, Chen Qiao, Jian Liu

Abstract read
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In one paragraph

Article in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Beibei WuDepartment of Information and TCM Big Data Innovation Lab of Beijing Office of Academic Research and Department of Oncology and Department of Scientific Research, China Academy of Chinese Medical Science, Xiyuan Hospital, Beijing, 10091, China.
Tong ZhangDepartment of Information and TCM Big Data Innovation Lab of Beijing Office of Academic Research and Department of Oncology and Department of Scientific Research, China Academy of Chinese Medical Science, Xiyuan Hospital, Beijing, 10091, China.
Ning DaiDepartment of Information and TCM Big Data Innovation Lab of Beijing Office of Academic Research and Department of Oncology and Department of Scientific Research, China Academy of Chinese Medical Science, Xiyuan Hospital, Beijing, 10091, China.
Ding LuoDepartment of Information and TCM Big Data Innovation Lab of Beijing Office of Academic Research and Department of Oncology and Department of Scientific Research, China Academy of Chinese Medical Science, Xiyuan Hospital, Beijing, 10091, China.
Xuejie WangDepartment of Information and TCM Big Data Innovation Lab of Beijing Office of Academic Research and Department of Oncology and Department of Scientific Research, China Academy of Chinese Medical Science, Xiyuan Hospital, Beijing, 10091, China.
Chen QiaoDepartment of Information and TCM Big Data Innovation Lab of Beijing Office of Academic Research and Department of Oncology and Department of Scientific Research, China Academy of Chinese Medical Science, Xiyuan Hospital, Beijing, 10091, China.
Jian LiuDepartment of Information and TCM Big Data Innovation Lab of Beijing Office of Academic Research and Department of Oncology and Department of Scientific Research, China Academy of Chinese Medical Science, Xiyuan Hospital, Beijing, 10091, China. xyyylj@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study conducts a systematic bibliometric analysis of tongue cancer publications to identify key topics, hotspots, and research distribution.

methodsWe analyzed tongue cancer publications in the Web of Science core collection database, assessing their quantity and quality. We investigated contributors, including countries, affiliations, journals, authors, and categories, within collaborative networks. Additionally, we synthesized key research findings using various analytical techniques, such as alluvial flow, burstness analysis, cluster analysis, co-occurrence network of associations, and network layer overlay.

resultsFrom 2000 to 2022, this bibliometric study covers 2205 articles and reviews across 617 journals, involving 72 countries, 2233 institutions, and 11,266 authors. It shows consistent growth, particularly in 2016. Key contributors include China (499 publications), Karolinska Institute (84 publications), Oral Oncology (144 publications), and Tuula Salo (47 publications). Other notable contributors are the USA (16,747 citations), the National Cancer Institute (NCI) (2597 citations), and the Memorial Sloan-Kettering Cancer Center (MSK) (2231 citations). Additionally, there are significant teams led by Tuula Salo and Dalianis. We have identified six primary clusters: #0 apoptosis, #1 depth of invasion, #2 radiotherapy, #3 hpv, #4 tongue cancer, #5 oral cancer. The top ten highly cited documents primarily pertain to epidemiology, prognostic indicators in early-stage oral tongue cancer, and HPV. Additionally, we observed 16 reference clusters, with depth of invasion (#3), young patients (#4), and tumor budding (#6) gaining prominence since 2012, indicating sustained research interests.

conclusionsThis analysis emphasizes the increasing scholarly interest in tongue cancer research. The bibliometric evaluation highlights pivotal recent research themes such as HPV, depth of invasion, tumor budding, and surgical margins. CLINICAL RELEVANCE: The bibliometric analysis highlights the key topics and studies which have shaped the understanding and management of tongue cancer.

Indexed as

Mouth NeoplasmsPapillomavirus InfectionsTongue NeoplasmsBibliometricsHumansTongueBibliometricsData visualizationHotspotsResearch statusTongue cancer

Identifiers

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Registered trials

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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.