Traumatic spinal cord and spinal column injuries: A bibliometric analysis of the 200 most cited articles
Bibliographic record
Abstract
Study Design: Bibliometric analysis. Objectives: This study aimed to highlight the 200 most influential articles related to traumatic spinal cord and spinal column injuries and provide an insight of past and current global trends in spinal trauma research. Methods: The Web of Science database was used to identify the top 200 most cited articles on the topic of traumatic spinal cord injury (SCI) and spinal column injuries between using a prespecified algorithm. The articles were manually reviewed; bibliometrics were collected on title, first and corresponding authors' country, institution, journal, publication year, and citation data. Results: The search string yielded 30,551 articles during 1977-2019. The average time from the publication was 19.5 years. A total of 1356 authors contributed to 67 different journals, the top 200 most cited articles amassing a total of 88,115 citations and an average 440.6 citations. The United States of America (USA) contributed the most with 110 articles; the top institution was the University of Toronto with 34 publications. Most studies focused on basic science research on SCI. Keyword analysis revealed the most commonly used keywords: SCI, inflammation, apoptosis, incidence/prevalence, and regeneration; four word-clusters were identified. Institutions from the USA and Canada collaborated the most and two major and two minor institutional collaboration subnetworks were identified. Co-citation analysis detected three main clusters of authors. Conclusion: This overview of the most cited articles on traumatic spinal cord and spinal column injuries provides insight into the international spinal trauma community and the terrain in this field, potentially acting as a springboard for further collaboration development.
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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.207 | 0.183 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".