Fifty Years of Cervical Myelopathy Research: Results from a Bibliometric Analysis
Bibliographic record
Abstract
We performed bibliometric analysis of the research papers published on clinical cervical spondylotic myelopathy (CSM) in the last 50 years. We extracted bibliometric data from Scopus and PubMed from 1970 to 2020 pertaining to clinical studies of CSM. The predominant journals, top cited articles, authors, and countries were identified using performance analysis. Science mapping was also performed to reveal the emerging trends, and conceptual and social structures of the authors and countries. Bibliometrix R-package was deployed for the study. The total numbers of clinical studies available in PubMed and Scopus were 1,302 and 3,470, respectively. The most cited article was published by Hilibrand AS, as observed in Scopus. Regarding the conceptual structure of the research, two main research themes were identified, one involving symptomatology, scientific-scale-based objective evaluation of symptoms, and surgical removal of the offending culprit, while the other was based on patho-etiology, relevant diagnostic modalities, and the surgery commonly performed for CSM. In terms of emerging trends, in recent times there is an increasing trend of scale-based objective evaluations, along with investigations of advanced nonoperative management. The United States is the most productive country, whereas Canada tops the list for inter-country collaboration. The trend of research showed a shift toward noninvasive procedures.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.161 | 0.192 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".