Visualized Analysis of Global Studies on Cervical Spondylosis Surgery: A Bibliometric Study Based on Web of Science Database and VOSviewer
Bibliographic record
Abstract
Abstract Purpose This study used multiple type of bibliometric analysis for identifying and summarizing the publications regarding cervical spondylosis surgery, for clarifying the history of this field, predicting the future hotspots of this field and improving communication among researchers. Methods Publications from Web of Science database between 1900 and 2019 were downloaded and analyzed by Excel 2016 and VOSviewer. Bibliometric maps of co-citations and maps of co-occurrence of keywords are constructed by VOSviewer software. Results A total of 2110 publications were searched from Web of Science. The total sum of times cited is 40448 with the average citation per publication of 19.17 times. USA published most papers (652, 30.9%). The most productive organizations is University of Toronto (96 publications). Spine (308 publications) published the most publications in this field. In co-citations of references analysis, four clusters of references are constructed by VOSviewer. In co-occurrence of keywords analysis, three clusters of keywords are constructed by VOSviewer. The latest keyword “degenerative cervical myelopathy” appeared in 2017 in 42 papers. Other relatively new keywords include “surgical outcomes”, “association”, “sagittal alignment”, “prognostic-factors” that appeared in 2016 in 33, 31, 34 and 37 papers respectively. Conclusion USA dominates the research regarding cervical spondylosis surgery. University of Toronto is the most productive organization in this field. Spine, European Spine Journal and Journal of Neurosurgery Spine are the top three productive journals on publications of cervical spondylosis surgery. “Degenerative cervical myelopathy”, “surgical outcomes”, “association”, “sagittal alignment” and “prognostic-factors” may be the new research hotspots in this field.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.162 | 0.183 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".