A Global Bibliometric Study of Spinal Arachnoiditis: Research Trends and Future Directions
Bibliographic record
Abstract
BACKGROUND: Spinal arachnoiditis (SA) involves chronic inflammation of the spinal arachnoid membrane, often due to surgery, trauma, infections, or autoimmune issues. It leads to ongoing pain and sensory disturbances in the back and lower limbs, along with possible bladder and bowel issues. Treatments focus on symptom relief and improving life quality. Despite growing research interest, a comprehensive analysis of SA's research trends is missing. This study uses bibliometric analysis to explore SA research trends, offering guidance for future research directions. METHODS: The study analyzed SA-related literature from the Web of Science Core Collection database between 2011 and 2024. It used bibliometric tools like VOSviewer and CiteSpace to assess publication trends, key contributors, influential journals, and keyword relationships, as well as citation patterns. RESULTS: The study found an increasing trend in SA-related publications. The United States leads in contributions, and the University of Toronto in Canada and King George's Medical University in India are among the top contributing institutions. The research involves 1152 authors, notably Marcus A. Stoodley. It covers fields like neurosurgery, neurology, psychiatry, and anesthesiology. Keywords highlight focal points in SA's etiology, pathogenesis, diagnosis, and treatment. Citations identify influential papers and cutting-edge research. CONCLUSIONS: This study provides the first extensive bibliometric overview of SA research, examining trends, hotspots, and future paths. It covers 7 key areas: from fundamental and pathogenesis research to personalized medicine and public education, reflecting a shift toward clinical applications and social strategies. The goal is to enhance understanding and treatment of SA.
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 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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.158 | 0.325 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| 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".