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Record W4409875715 · doi:10.2196/71935

Global Trends in Cadaver Donation and Medical Education Research: Bibliometric Analysis Based on VOSviewer and CiteSpace

2025· article· en· W4409875715 on OpenAlexvenueaboutno aff
Xianxian Zhou, Hua Xiong, Dexi Hu

Bibliographic record

VenueJMIR Medical Education · 2025
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintPsychologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: The cadaver serves as a crucial resource in medical education, research, and clinical practice, as well as a vital foundation for fundamental medical experimental teaching. Objective: This study aims to use bibliometric analysis to create a knowledge map of cadaver donation in medical education, identify global trends, anticipate future research directions, and offer a foundation for upcoming investigations. Methods: Articles and review papers concerning cadaver donation and medical education, with a final search cutoff of January 10, 2025, were systematically retrieved from the Web of Science Core Collection database. Two reviewers carefully examined the initial set of articles based on titles and abstracts to exclude irrelevant ones. A quadratic regression model was used to examine the annual publication data. The model's goodness of fit was assessed using the R2 value, and the statistical significance of the findings was determined through the P valu. The selected publications were then analyzed and visualized for country, institution, author, reference, journal, and keywords using CiteSpace 6.3R3, VOSviewer 1.6.19, and the Online Analysis Platform of the Literature Metrology Database. Results: The quadratic regression model yielded the equation Y=0.1586X²-633.9X+633395, indicating a substantial increase in the number of publications over time (R2=0.9575, P<.05). The model forecasts that the publication count will reach 107 by 202. This upward trend is statistically significant, highlighting a notable rise in research interest and activity within this field over time. The United States was a major contributor, accounting for 21.2% (303/1114) of all publications. In terms of continents and faiths, Europe and Christianity contributed the most, while McGill University and The University of Sydney were the leading institutions. Prominent authors in this field included De Caro Raffaele, Macchi Veronica, Porzionato Andrea, Stecco Carla, and Dhanani Sonny. The most frequently cocited reference was "Bodies for Anatomy Education in Medical Schools: An Overview of the Sources of Cadavers Worldwide." The journal Anatomical Sciences Education published the most articles in this area and received the highest citation count. Cluster analysis of keywords revealed that "kidney transplantation," "gross anatomy education," and "brain death" were key research topics, while burst analysis of keywords identified "public perception" and "anatomical science" as emerging areas of investigation. Conclusions: This research presents a distinctive bibliometric approach to cadaver donation within medical education, setting it apart from previous studies by delivering an extensive global overview of trends and influential contributors in this domain. The results emphasize the increasing global interest and collaborative efforts surrounding cadaver donation, while also offering fresh perspectives on emerging topics like public perception and anatomical sciences. This paper serves as an important reference for researchers, policymakers, and educators, supporting the development of future strategies to enhance cadaver donation programs and further medical education.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0380.093
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.392
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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