Study on neural stem cells and spinal cord injury: Based on bibliometric analysis
Bibliographic record
Abstract
Objective: This study aims to explore the application of neural stem cells (NSCs) in the treatment of spinal cord injury (SCI) through a bibliometric analysis, identifying global research trends and hotspots in this field to further promote research progress. Methods: The bibliometric analysis was conducted using data from the Web of Science (WOS) database, focusing on literature published between 2000 and 2024. A total of 7010 related documents were retrieved, with 4531 specifically addressing NSCs and SCI. The analysis utilized the VOSviewer data analysis platform and the bibliometric online analysis platform to visualize publication and citation trends, keyword frequencies, and the contributions of various countries, authors and journals to the field. Results: The analysis revealed that the United States leads in SCI research, producing the most related articles, followed by China. Key research themes identified include “central nervous system”, “transplantation”, and “differentiation”. The most prolific journals in this field are the Cell Transplantation and Experimental Neurology, with the highest average citation rates observed in Nature Medicine. Okano H and Univ Toronto are the most influential author and institution, respectively. Conclusion: The bibliometric analysis underscores the growing global attention to SCI research and the importance of NSCs as a promising treatment avenue. Despite the challenges in understanding the SCI microenvironment and achieving clinical translation, significant progress has been made in stem cell preparation, in vitro culture, and differentiation methods. The study suggests that ongoing research and the development of comprehensive treatment strategies will enhance the clinical application of NSCs, offering hope for improved outcomes in SCI patients.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.148 | 0.190 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".