Bibliometric Analysis of Research Trends in Spinal Cord Injury Rehabilitation: Mapping the Landscape of Scientific Publication
Bibliographic record
Abstract
Background Research trends in Spinal Cord Injury (SCI) rehabilitation remain relatively unexplored within the scientific literature. Despite increasing interest, there is a notable lack of comprehensive bibliometric analysis to map and synthesize global research trends in SCI rehabilitation, especially over the last three decades. Materials and Methods We conducted a detailed bibliometric analysis using data extracted from the Web of Science (WoS) Core Collection and Scopus databases, covering the period from 1993 to 2023. The analysis employed both qualitative and quantitative approaches, utilizing bibliometric software tools such as VOSviewer to examine publication outputs, journals, authors, institutions, countries, cited references, keywords, and emerging terms in the field. Specific attention was given to identifying trends in technological interventions and research frontiers in SCI rehabilitation. Results This study identified 1,377 unique articles for further analysis. The United States emerged as the leading contributor to SCI research, followed by Canada and Australia. Among institutions, the University of Toronto was the most active, with significant contributions also from the University of Groningen and the University of British Columbia. The top research fields in SCI rehabilitation were Neuroscience and Neurology, followed by Sports Science and General and Internal Medicine. Additionally, this study highlighted key thematic areas shaping the field, including recovery and function, social and psychological aspects, neurological and medical complications, assessment and measurement, and psychological well-being. Conclusion This bibliometric study underscores SCI rehabilitation as a mature and expanding research field with significant global collaboration. However, there is a pressing need for higher-quality research to further advance the field. Our findings offer valuable insights for researchers to shape future research directions and enhance the impact of SCI rehabilitation studies.
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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 | 0.030 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.239 | 0.314 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".