ArticleMedicine2025
Mapping intellectual structure and research hotspots of cancer studies in primary health care: A machine-learning-based analysis.
Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Septic Shock in Hematological Malignancies: Role of Artificial Intelligence in Predicting Outcomes.Current oncology (Toronto, Ont.) · 2025Review
- Science mapping of COVID-19 contributions in primary health care by OECD countries: A machine learning approach.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the contemporary fight against cancer, primary health care (PHC) services hold a significant and critical position within the healthcare system. This study, as one of the most detailed investigations into cancer research in primary care, comprehensively evaluates cancer studies from the perspective of PHC using bibliometric techniques and machine learning. The dataset for the analyses was sourced from the Web of Science (WoS) Core Collection database on March 20, 2024. The Bibliometrix package within the R programming environment, alongside the Biblioshiny application, and VOSViewer software were employed for the bibliometric analyses. In this study, Latent Dirichlet Allocation was utilized as a prominent topic modeling algorithm. The implementation of this technique utilized Python along with the SciKit-Learn and Gensim libraries, ensuring robust model development and evaluation. The 2040 articles were produced by a total of 6705 different authors, 2166 different affiliations, and 75 different countries. Cancer survivors are more vulnerable and need more sensitive health services. The most intensively studied 3 cancer types in the PHC, listed by prevalence, are colorectal cancer, breast cancer, and cervical cancer. Additionally, prominent research topics in PHC include cancer screening, diagnosis, early detection, prevention, education, genetic factors and family history, risk factors, symptoms/signs, preventive medicine, referral and consultation, chronic disease management and health services research for cancer patients, health care disparities, palliative care, and communication with patients in PHC. Family physicians, being the first point of contact with the public, play a crucial role in preventing cancer cases, caring for patients with active cancer diagnoses, supporting cancer survivors in their post-cancer lives, and identifying and referring cancer cases at the earliest stages. However, cancer has many types, each with its own distinct symptoms, as well as similar types to each other. At this point, periodic educational training for doctors on cancer by health authorities, regular publication of cancer-related guidance resources by the central healthcare system, development of integrated decision support tools used by physicians during patient care, and the creation of informative mobile applications for cancer prevention or post-cancer life for patients have been considered highly critical.
Indexed as
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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.