Research hotspots and trend analysis of cell transplantation in traumatic spinal cord injury: a bibliometric and visualized analysis
Bibliographic record
Abstract
Background: A traumatic spinal cord injury (TSCI) can lead to severe nerve damage and disability. Cell transplantation therapy has shown great potential in the reconstruction of damaged spinal cords and promoting functional recovery. However, there is a lack of frontiers and futures analysis in the study of cell transplantation in TSCI. Methods: We used CiteSpace, VOSviewer and biblilometrix R package to perform bibliometric analysis on cell transplantation in TSCI from 2013 to 2023. Bibliometric records were extracted from English articles and reviews from the Web of Science core collection. Results: The bibliometric analysis included 284 papers published in 154 journals by 1,780 authors from 487 institutions in 41 countries and regions. The number of articles published in the past decade has fluctuated slightly, while the number of article citations has steadily increased. Mainland China and the United States are the leading countries and regions in this field, with the National Natural Science Foundation of China being the most funded foundation, and the United States being the country with the most funded articles. The University of Toronto in Canada is a prolific institution. Michael G. Fehlings has published the most articles, and D Michele Basso is the most cited author. Cell transplantation is the most published journal, and the Journal of Neurotrauma is the most cited journal. Cell and tissue engineering and clinical neurology are the basic disciplines in this field, and cutting-edge disciplines include developmental biology, biochemistry and molecular biology, and materials science and multidisciplinary. This study also helps scholars understand the current hotspots and future trends in this field. Marrow stromal cells, glial progenitor, and cell therapy are current research hotspots in this field, while nerve regeneration, cell therapy, and the safety of transplantation of transplantation may be potential research directions in the future. Conclusion: Cell transplantation after TSCI is receiving increasing attention. Cell therapy is both the frontier and a possible future trend in TSCI research. In addition, glial progenitor and marrow stromal cells are also current research hotspots. Meanwhile, nerve regeneration and safety of transplantation may be potential research directions. These findings will help further deepen research on cell transplantation for TSCI in scientific work.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.147 | 0.176 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".