Worldwide productivity and research trend of publications concerning electroactive materials and spinal cord injury: A bibliometric study
Bibliographic record
Abstract
Purpose: We investigated the current state and trends in the area during the previous 10 years using bibliometric approaches to evaluate the global scientific output of research on electroactive materials and spinal cord injury. Methods: Studies on spinal cord injury in electroactive materials that were published between 2012 and 2022 were located using the Web of science (WOS) datebase. The software programs bibliometrix R-package and CiteSpace were used to do quantitative analyses of annual publications, nation, author, institution, journal source, co-cited references, and keywords. The studies were categorized by the research’s main points using a qualitative analysis, and publications having more than 10 citations each year. Results: In the final analysis, 1,330 relevant papers or reviews were included. There is an increased tendency in both the average annual citation rate and the number of publications in the discipline. The United States and the University of Toronto are the countries and institutions that have contributed the most to this discipline, respectively. The majority of authors are from the China and United States. Zhang Y is the author with the most published articles and holds the top position in the cited author h-index species. The journal with the highest number of published articles is “Disability and rehabilitation”; the journal is divided into four main areas including physics, materials, chemistry, molecular, and biology. The keyword analysis revealed a shift in research hotspots from schwann cell, fracture, and urinary disorders to carbon-based materials, functional recovery, and surgery. Analysis of qualitative data revealed that the role and mechanism of injectable conductive hydrogels in spinal cord healing after damage is a hot topic of current study, with the mechanism primarily focusing on the inhibition of oxidative stress (Nrf2) and apoptosis (Casepase 3). Conclusion: Our bibliometric analysis indicates that research on electroactive materials for spinal cord injury remains an active field of study. Moreover, contemporary research is concentrated on carbon-based materials, functional rehabilitation, and surgery.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.126 | 0.177 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| 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".