Global Research Status and Trends in Venous Thromboembolism After Hip or Knee Arthroplasty From 1990 to 2021: A Bibliometric Analysis
Bibliographic record
Abstract
Background Venous thromboembolism (VTE) after hip or knee arthroplasty has attracted increasing attention over the past few decades. However, there is no bibliometric report on the publications in this field. The purpose of this study was to analyze the global research status, hotspots, and trends in VTE after arthroplasty. Methods All articles about VTE research after hip or knee arthroplasty from 1990 to 2021 were retrieved from the Web of Science Core Collection database. The information of each article including citation, title, author, journal, country, institution, keywords, and level of evidence was extracted for bibliometric analysis. Results A total of 1,245 original articles from 53 countries and 603 institutions were retrieved. The USA contributed most with 457 articles, followed by England and Canada. McMaster University in Canada was the leading institution for publications. The journals with the highest output and citation were the Journal of Arthroplasty and the Thrombosis and Haemostasis, respectively. The median number of citations was significantly different among the levels of evidence (F = 128.957, P < 0.001). The research hotspots switched from VTE diagnosis and heparin to factor Xa inhibitors (fondaparinux, rivaroxaban, apixaban) and direct thrombin inhibitors (dabigatran etexilate, ximelagatran), and finally to aspirin, risk factor studies, which can be observed from the keyword analysis and co-cited reference cluster analysis. Conclusions This study observed an increasing trend of research articles on VTE after arthroplasty. Publications with higher levels of evidence gained further popularity among researchers and orthopedic surgeons. Additionally, individualized VTE prevention and the development of new, safe, effective, and inexpensive oral agents would be emerging trends in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.133 | 0.171 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".