The global state of research in stem cells therapy for spinal cord injury (2003–2022): a visualized analysis
Bibliographic record
Abstract
Objective: Our study aimed to visualize the global status and frontiers in stem cell therapy for spinal cord injury by using bibliometric methodology. Methods: Publication citation information related to stem cell therapy for spinal cord injury (SCI) studies between 2003 and 2022 was retrieved from the Web of Science Core Collection database. For the visualized study, VOS viewer software and Graph Pad Prism 9.5 were used to perform bibliometric analysis of included data and publication number statistics in stem cell therapy for the SCI domain. Results: A total of 6,686 publications were retrieved. The USA and China made the highest contributions to global research with the highest number of citations and link strength. The journal Experimental Neurology ranks as the top journal, combining the publication amount and bibliometrics results. The University of Toronto, based in Canada, was the first-ranking institution. The directions of the current study could be divided into five clusters. The research of Transplantation and Regenerative Medicine and Neurosciences Mechanism Research may be the emerging frontiers in this domain. Conclusion: In summary, stem cell therapy for spinal cord injuries is poised for more valuable advances.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.067 | 0.110 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".