Global Trends and Hotspots of Research on the Inflammatory Mechanisms for Spinal Cord Injury: A Bibliometric Analysis
Bibliographic record
Abstract
OBJECTIVE: Excessive inflammatory responses following spinal cord injury (SCI) contribute to poor prognosis. Hence, the inflammatory mechanisms underlying SCI development are being increasingly explored. By conducting a bibliometric analysis, this study investigated global research trends and emerging hotspots in this domain. METHODS: Relevant studies published between January 1, 2005, and February 28, 2025, were retrieved from the Web of Science Core Collection database. Quantitative bibliometric analysis was conducted using CiteSpace, VOSviewer, and the Bibliometrix package in R software. RESULTS: A total of 7198 relevant publications were analyzed. The number of publications showed a steadily increasing trend from 2005 to 2022, with a slight decline in 2023 and 2024. China was the leading country in publication volume, while Canada ranked first in average citations, indicating strong research influence. Zhejiang University had the highest publication count, whereas Ohio State University ranked first in both total and average citations. Journal of Neurotrauma was the most prolific journal, and Cuzzocrea Salvatore emerged as the most productive author. Keyword analysis revealed hydrogels, exosomes, and signaling pathways as key research hotspots. CONCLUSIONS: Substantial progress has been achieved in understanding the inflammatory mechanisms underlying SCI development. The volume of related publications continues to increase, predominantly in China and the United States. Researchers from the United States and Canada have the highest academic influence. Emerging research hotspots include hydrogels, exosomes, and signaling pathways.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.143 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".