ArticleTherapeutic advances in gastroenterology2020
A bibliometric analysis of 23,492 publications on rectal cancer by machine learning: basic medical research is needed.
Article in Therapeutic advances in gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled 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.
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
20 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence in colorectal surgery: an AI-powered systematic review.Techniques in coloproctology · 2023Pooled it
- Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review.Frontiers in endocrinology · 2023Pooled it
- A comprehensive overview of psoriatic research over the past 20 years: machine learning-based bibliometric analysis.Frontiers in immunology · 2023Pooled it
- Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years.Frontiers in oncology · 2022Pooled it
- Publication Counts in Context: Normalization Using Query and Reference Terms in PubMed.Journal of medical Internet research · 2025Article
- Research trends on AI in breast cancer diagnosis, and treatment over two decades.Discover oncology · 2024Review
- Multi-Algorithm Analysis Reveals Pyroptosis-Linked Genes as Pancreatic Cancer Biomarkers.Cancers · 2024Article
- A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis.Frontiers in oncology · 2023Review
- A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years.Frontiers in medicine · 2023Article
- A comprehensive overview of cellular senescence from 1990 to 2021: A machine learning-based bibliometric analysis.Frontiers in medicine · 2023Article
- Research topics and hotspot trends of lumbar spondylolisthesis: A text-mining study with machine learning.Frontiers in surgery · 2022Article
- Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis.Frontiers in public health · 2022Article
- Commentary: Radiomics in oncology: A 10-year bibliometric analysis.Frontiers in oncology · 2022Article
- Bibliometric analysis of the published literature on machine learning in economics and econometrics.Social network analysis and mining · 2022Article
- Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing.Frontiers in psychiatry · 2022Article
- Psycho-Oncology: A Bibliometric Review of the 100 Most-Cited Articles.Healthcare (Basel, Switzerland) · 2021Review
- A Bibliometric Analysis of 14,822 Researches on Myocardial Reperfusion Injury by Machine Learning.International journal of environmental research and public health · 2021Article
- A Bibliometric Analysis of 34,692 Publications on Thyroid Cancer by Machine Learning: How Much Has Been Done in the Past Three Decades?Frontiers in oncology · 2021Article
- Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis.Therapeutic advances in gastroenterology · 2021Review
- Exploring the landscape, hot topics, and trends of bariatric metabolic surgery with machine learning and bibliometric analysis.Therapeutic advances in gastrointestinal endoscopyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsThe aim of this study was to analyse the landscape of publications on rectal cancer (RC) over the past 25 years by machine learning and semantic analysis.
methodsPublications indexed in PubMed under the Medical Subject Headings (MeSH) term 'Rectal Neoplasms' from 1994 to 2018 were downloaded in September 2019. R and Python were used to extract publication date, MeSH terms and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation was applied to analyse the text from the articles' abstracts to identify more specific research topics. Louvain algorithm was used to establish a topic network resulting in identifying the relationship between the topics.
resultsA total of 23,492 papers published were identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies.
conclusionsThe number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future.
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.