The 100 Top-cited Articles on Cervical Spine Surgery
Bibliographic record
Abstract
STUDY DESIGN: Bibliometric analysis. OBJECTIVE: To identify and analyze the top 100 most-cited articles in cervical spine surgery, highlighting influential research, key authors, and major contributing institutions. SUMMARY OF BACKGROUND DATA: Cervical spine surgery has evolved significantly with advancements in surgical techniques and technology. Bibliometric analysis provides insights into the most impactful research, shaping clinical practice and guiding future studies. METHODS: A bibliometric analysis was conducted using the Web of Science database (1900-April 2024). Articles were identified using the search terms "cervical," "spine," and "surgery," then ranked by citation count. The top 100 most-cited articles were selected based on relevance. Extracted variables included title, year, authors, affiliations, journal, keywords, and citation count. Data analysis was performed using VOSviewer and Bibliometrix. RESULTS: The top 100 articles, published between 1954 and 2016 across 32 journals, were authored by 523 researchers, with only two single-author publications. The articles included 86 original research papers and 14 reviews, with a mean citation count of 410.2 per article and a total citation count of 41,002. Spine published the most articles (34), followed by the Journal of Bone and Joint Surgery (11) and the Journal of Neurosurgery (7). The University of California System (53 articles) and the University of Toronto (29 articles) were the most prolific institutions. Abumi K. and Kaneda K. were the most frequently published authors, each contributing five articles. A 20% international co-authorship rate was observed, indicating extensive global collaboration. Network and cluster analyses identified stabilization, fusion, and decompression as primary research themes. CONCLUSION: This study highlights the bibliometric impact of technological and procedural innovations in cervical spine surgery. The findings emphasize the field's collaborative nature and the dominant role of North American institutions in advancing cervical spine surgery research, providing a foundation for future clinical and academic developments.
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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 | 0.076 | 0.413 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.178 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".