A Bibliometric Analysis of Research Trends in Neck Pain from 2000 to 2025
Bibliographic record
Abstract
Objective: This paper conducts a bibliometric analysis of the literature on neck pain research from 2000 to 2025, aiming to comprehensively and systematically understand the research landscape, hotspots, and frontier trends in this field, providing a reference for future research directions. Methods: Data were sourced from the Web of Science Core Collection, with the search term TI = “neck pain,” covering the time span from 2000 to 2025, resulting in 2746 articles. Software such as CiteSpace V6.3.R1 and VOSviewer 1.6.20 was used to analyze publication volume, countries, authors, institutions, keywords, and co-citation networks. Results: The number of publications in neck pain research has been increasing year by year, indicating a rising level of research activity. Authors like Falla, D, Jull, G, and institutions such as Univ Queensland and Univ Toronto have significant influence in this field. Co-occurrence analysis of keywords shows that “neck pain,” “low back pain,” and “disability index” are high-frequency keywords, reflecting research hotspots such as the characteristics and treatment of neck pain and its interrelation with pain in other regions. Timeline analysis and keyword emergence analysis reveal the frontiers and development trends in this field, such as the growing attention on emerging therapeutic methods like “exercise therapy” and “dry needling,” while keywords like “intensity,” “individuals,” and “quality” indicate an increasing emphasis on personalization, precision, and quality control in the treatment process. Conclusion: The field of neck pain research is continuously expanding and deepening. Future research should further investigate the pathogenesis of neck pain, its associations with other conditions, the refinement of assessment methods, and the development of innovative rehabilitation strategies. Emphasis should also be placed on interdisciplinary collaboration to provide more robust theoretical foundations and practical guidance for the clinical treatment and rehabilitation management of neck pain.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.178 | 0.229 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".