ArticleGynecologic oncology2023
Use of topic modeling to assess research trends in the journal Gynecologic Oncology.
Article in Gynecologic oncology, 2023. 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.
- Thematic Structure of Clinical Rehabilitation Research in the Early Post-Pandemic Period: An LDA-Based Analysis of Publications from 2022 to 2025.Healthcare (Basel, Switzerland) · 2026Article
- Mapping Health Research Involving Human Subjects in Saudi Arabia:Saudi medical journal · 2026Article
- CENTRA: knowledge-based gene contextuality graphs reveal functional master regulators by centrality and fractality.NAR genomics and bioinformatics · 2025Article
- Exploiting Natural Language Processing to Unveil Topics and Trends of Traumatic Brain Injury Research.Neurotrauma reports · 2024Article
- Mapping two decades of research in rheumatology-specific journals: a topic modeling analysis with BERTopic.Therapeutic advances in musculoskeletal disease · 2024Article
- Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
STUDY
objectiveThere is scant research identifying thematic trends within medical research. This work may provide insight into how a given field values certain topics. We assessed the feasibility of using a machine learning approach to determine the most common research themes published in Gynecologic Oncology over a thirty-year period and to subsequently evaluate how interest in these topics changed over time.
methodsWe retrieved the abstracts of all original research published in Gynecologic Oncology from 1990 to 2020 using PubMed. Abstract text was processed through a natural language processing algorithm and clustered into topical themes using latent Dirichlet allocation (LDA) prior to manual labeling. Topics were investigated for temporal trends.
resultsWe retrieved 12,586 original research articles, of which 11,217 were evaluable for subsequent analysis. Twenty-three research topics were selected at the completion of topic modeling. The topics of basic science genetics, epidemiologic methods, and chemotherapy experienced the greatest increase over the time period, while postoperative outcomes, reproductive age cancer management, and cervical dysplasia experienced the greatest decline. Interest in basic science research remained relatively constant. Topics were additionally reviewed for words indicative of either surgical or medical therapy. Both surgical and medical topics saw increasing interest, with surgical topics experiencing a greater increase and representing a higher proportion of published topics.
conclusionsTopic modeling, a type of unsupervised machine learning, was successfully used to identify trends in research themes. The application of this technique provided insight into how the field of gynecologic oncology values the components of its scope of practice and therefore how it may choose to allocate grant funding, disseminate research, and participate in the public discourse.
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.