Global Research Trends in Spinal Biomechanics: A Bibliometric and Visual Analysis
Bibliographic record
Abstract
Research in Spinal Biomechanics has gained increasing attention. This study aims to investigate the current status and trends in this field globally. Publications on Spinal Biomechanics from January 1, 2005, to November 1, 2024, were retrieved from the Web of Science - Science Citation Index Expanded. Bibliometric methods were used to analyze the source data, and VOSviewer version 1.6.19 software was employed for co-authorship, co-occurrence, bibliographic coupling, and co-citation analyses. The overall trends in Spinal Biomechanics research in recent years were also analyzed. A total of 3,812 articles were identified. The number of global research and publications on Spinal Biomechanics has increased annually. The United States contributes the most to global research in this field, with the highest number of citations and the highest h-index. The Journal of Biomechanics and Clinical Biomechanics have the highest publication rates. The University of British Columbia, the University of Montreal, the University of Pittsburgh, and St. Joseph's Hospital are the top four contributing institutions. Research can be classified into four categories: sports biomechanics, tissue engineering, clinical research, and mechanism research. Clinical research is predicted to be the next hot topic in this field. Based on current global research trends, the number of publications related to Spinal Biomechanics is expected to continue increasing. The United States is currently the largest contributor to research in this field. Most research efforts will focus on clinical studies of Spinal Biomechanics, which may be the next hotspot in this research area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.106 | 0.073 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".