Global Research Trends on Gait Rehabilitation in Individuals With Spinal Cord Injury- A Bibliometric Analysis
Bibliographic record
Abstract
Study DesignBibliometric analysis.ObjectiveThe study aims to comprehensively assess the literature related to gait rehabilitation for individuals with spinal cord injury (SCI) to identify significant contributors, and to explore the collaborations and emerging themes in the field.MethodsOriginal and review articles in English using relevant keywords were searched in the Clarivate Web of Science database. The data from the selected articles were imported into R software. Bibliometric indicators were assessed to determine author contributions, country affiliations, journal sources, and thematic trends.ResultsA total of 1313 relevant articles were identified. The USA, followed by Canada and Switzerland were the most prolific countries contributing to gait rehabilitation research in SCI. The most relevant journals were Spinal Cord, Archives of Physical Medicine and Rehabilitation, Journal of Spinal Cord Medicine, Journal of NeuroEngineering, and Journal of Neurotrauma. The highest contributions came from Northwestern University, the University of Miami, and the University of Alberta. The analysis revealed an increase in research interest in gait rehabilitation after 2000, with a focus on interdisciplinary approaches and emerging technologies like robotics, exoskeletons, and neuromodulation.ConclusionThe analysis demonstrates the importance of collaborative and interdisciplinary research in gait rehabilitation. The results indicate a shift in research focus from traditional methods to the integration of technology. The impact of publications from the USA and Europe is a notable finding. The study highlights the growth of articles related to technology-driven approaches and understanding neuroplasticity in gait rehabilitation.
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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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.234 | 0.241 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".