Mapping Theme Trends and Recognizing Hot Spots in Acute Spinal Cord Injury: A Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: Acute spinal cord injury causes severe motor and sensory dysfunction, significantly burdening individuals and society. This study uses bibliometric analysis to identify research trends and key areas, providing insights for future advancements in treatment. METHODS: Scientific publications on acute spinal cord injury were collected from PubMed and the Web of Science Core Collection between 2000 and 2022. Data were analyzed using Bibliometric, CiteSpace, and Bibliographic Item Co-occurrence Matrix Builder, with gCLUTO applied for co-word bicluster analysis based on MeSH term matrices. RESULTS: Two thousand five hundred thirteen publications on acute spinal cord injury were published, with the number of articles increasing annually from 38 to 268. Spinal cord has emerged as the leading journal in this field, and the United States maintains its dominant position in global research impact. The University of Toronto ranks first among research institutions, with significant contributions from researchers such as Fehlings and Kwon. Research on acute spinal cord injury primarily focuses on 7 key areas: metabolism, pharmacology, surgical timing, rehabilitation, pathology, clinical predictors, and diagnostic imaging. CONCLUSIONS: Our study reveals substantial growth in acute spinal cord injury research over the past 2 decades, emphasizing leading countries, researchers, institutions, and journals. Animal models remain pivotal in drug development for basic medicine and neuroscience. Consensus has been reached among experts regarding the timing of surgical intervention, while artificial intelligence and multidisciplinary approaches are emerging as promising avenues for comprehensive treatment. Additionally, ongoing research into spinal cord injury pathophysiology provides essential guidance for pharmacological and surgical treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.281 | 0.411 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".