A Bibliometric Analysis and Visualization of Current Research Trends in the Treatment of Cervical Spondylotic Myelopathy
Bibliographic record
Abstract
STUDY DESIGN: Bibliometric analysis. OBJECTIVE: Cervical spondylotic myelopathy (CSM) has become the most common cause of spinal cord dysfunction. Many topics of CSM still remain controversial. This study aimed to illustrate the overall knowledge structure and development trends of CSM. METHODS: Research data sets were acquired from the Web of Science database and the time span was defined as "2000 to 2019." VOS viewer and Citespace software was used to analyze the data and generate visualization knowledge maps. Annual trends of publications, distribution, H-index status, co-authorship status, and research hotspots were analyzed. RESULTS: . The cooperation between the countries, institutes, and authors were relatively weak. Cervical sagittal alignment, predictive factor, diffusion tensor imaging, and the natural history of CSM may become a frontier in this research field. CONCLUSION: The number of publications showed an upward trend with a stable rise. Most of the publications are limited to a few countries and institutions with relatively weak interaction. The United States, Canada, Japan, China, and India have made significant contributions to the field of CSM. The United States is the country with the highest productivity, not only in quality but also in quantity. Cervical sagittal alignment, predictive factor, diffusion tensor imaging, and the natural history of CSM are the research hotspots in the recent years.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.134 | 0.138 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".