Evidence map›Paper›PMID 42459056›Full record

ArticleAnalytical cellular pathology (Amsterdam)2026

Machine Learning-Based Text Processing Reveals Research Trends in Heterotopic Ossification.

Yangbai Sun, Qinyuan Zhu, Chaoran Yu, Chunmeng Wang, Wangjun Yan, Chaoyin Jiang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Analytical cellular pathology (Amsterdam), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Yangbai SunDepartment of Orthopedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, sjtu.edu.cn.ORCID https://orcid.org/0000-0002-2853-2813
Qinyuan ZhuDepartment of Dermatology, Huashan Hospital, Fudan University, Shanghai, China, fudan.edu.cn.ORCID https://orcid.org/0009-0008-8707-8420
Chaoran YuDepartment of General Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Division for Shanghai Ninth People's Hospital, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China, sjtu.edu.cn.ORCID https://orcid.org/0000-0003-4657-7975
Chunmeng WangDepartment of Musculoskeletal Oncology, Bone and Soft Tissue Sarcomas Centre, Fudan University Shanghai Cancer Centre, Fudan University, Shanghai, China, fudan.edu.cn.ORCID https://orcid.org/0000-0001-9440-6287
Wangjun YanDepartment of Musculoskeletal Oncology, Bone and Soft Tissue Sarcomas Centre, Fudan University Shanghai Cancer Centre, Fudan University, Shanghai, China, fudan.edu.cn.ORCID https://orcid.org/0000-0001-9178-0687
Chaoyin JiangDepartment of Orthopedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, sjtu.edu.cn.ORCID https://orcid.org/0000-0002-3760-9077

Funding

China Hospital Development Institute, Shanghai Jiao Tong University 2026 Annual Decision-Making Consultation Project CHDI-2026-Z-030Fundamental Research Program Funding of Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine JYZZ189National Natural Science Foundation of China 82003351National Natural Science Foundation of China 82172415Project of Hospital Management from Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine YGB202412Shanghai Municipal Health Commission 2025 Annual Commissioned Research Project on Health Legislation 6Shanghai Municipal Health Commission Research Project 20194Y0242
6 · The paper itself

Abstract

backgroundHeterotopic ossification (HO) represents a highly active research field in pathological bone formation. Despite substantial advancements, a comprehensive understanding of its underlying mechanisms and clinical trajectory remains incomplete. MATERIALS AND

methodsA bibliometric and machine-learning latent Dirichlet allocation (LDA) analysis was performed using data retrieved from the Web of Science Core Collection database.

resultsA total of 4722 publications related to HO were identified. The most prolific countries included the USA, CHINA, GERMANY, the UK, JAPAN, SOUTH KOREA, ITALY, TURKEY, FRANCE, and CANADA . GERMANY demonstrated the highest citation strength, followed by CHINA . Aside from "heterotopic ossification," other frequently occurring author keywords included "fibrodysplasia ossificans progressive," "complications" and "total hip arthroplasty." In keywords plus, besides HO, replacement, bone-formation, and arthroplasty were the most frequently occurring terms. Institutional network analysis with subject-specific clustering indicated that Shanghai Jiao Tong University was significantly enriched in radiology, nuclear medicine and medical imaging, while Wilderness Spine Serv specialized in surgical management. A developmental timeline plot of a network of most contributing authors also was visualized, along with the most influential references. Meanwhile, citation analysis indicated that Kaplan FS and Shore EM were the top-cited authors. By LDA analysis, a total of 16 key topics were identified in this field with distinct period-proportion visualization. One of the topics, cell bone express differentiation and formation has clearly dominated the last 10 years.

conclusionThis study constitutes the most extensive text processing analysis of HO to date, offering valuable insights and directions for future development.

Indexed as

Machine LearningOssification, HeterotopicBibliometricsHumansbibliometric analysisheterotopic ossificationmachine learningWeb of Science

Identifiers

PMID42459056
PMCPMC13373702

What OpenQuestion holds

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LicenceCC BY
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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.