The top 100 highly cited articles on neck pain: A bibliometric analysis
Bibliographic record
Abstract
Objective: Neck pain has emerged as a significant public health concern. This study is to unveil the present state of neck pain research, thereby furnishing invaluable insights for prospective research endeavours and clinical applications. Methods: The study was initiated by searching the Web of Science Core Collection database, focusing on "neck pain". From the amassed results, the top 100 most cited references were imported into CiteSpace and VOSviewer, enabling a rigorous bibliometric analysis. To ensure precision, synonymous terms conveying similar meanings were harmonized. The bibliometric study encompassed countries, research institutions, authors, journals, and keyword analysis. Results: The investigation centered on a curated compilation of 100 articles, disseminated across a diverse array of 36 scholarly journals. These seminal articles originated from 24 distinct countries, reflecting contributions from a wide spectrum of 188 research institutions. Impressively, a collaborative effort involving 385 authors emerged. Noteworthy core research countries included the United States and Australia, with the University of Queensland and the University of Toronto asserting notable influence. Prolific authors such as J. David Cassidy and Pierre Cote garnered attention. Present research endeavours pivot around the incidence of neck pain, the identification of risk factors, the efficacy evaluation of treatment modalities, and a pronounced focus on high-quality randomized controlled trials and systematic reviews. Conclusion: The study shines a light on key research countries, influential institutions, prominent authors, and prevalent trends, effectively contributing to comprehending the knowledge landscape and research dynamics in the field of neck pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.178 | 0.180 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".