50 years of methylprednisolone application in spinal cord injury: a bibliometric analysis
Bibliographic record
Abstract
PURPOSE: Methylprednisolone (MP) is a synthetic glucocorticoid known for its anti-inflammatory and immunosuppressive effects, yet its application in global spinal cord injury (SCI) research has not been thoroughly summarized. This study aims to assess the current status and trends of methylprednisolone research in SCI, providing insights for future scholarly work. METHODS: Articles on methylprednisolone in SCI published from 1975 to 2023 were retrieved from the Web of Science database. Metrics such as publication counts, H-index values, and data on countries, institutions, authors, and journals were analyzed. Co-citation, collaboration, and co-occurrence analyses of keywords were performed using CiteSpace. RESULTS: A total of 1,651 articles were identified, and publication numbers showed a consistent annual increase. The United States and Canada led in publication counts, H-index values, and citations, with the University of Toronto and the Veterans Health Administration being significant contributors. Bracken M.B. was the leading author. The most frequent keywords included 'trauma,' 'lipid peroxidation,' 'dose response,' 'ischemia,' and 'methylprednisolone.' A co-occurrence analysis classified 225 keywords into three clusters, highlighting key research areas in SCI. CONCLUSIONS: These findings offer valuable insights into authors, countries, institutions, keywords, and research hotspots in SCI over the past 50 years, guiding future research directions in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 0.001 |
| Bibliometrics | 0.166 | 0.316 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".