Global trends and hotspots related to whiplash injury: A visualization study
Bibliographic record
Abstract
Whiplash injury, commonly occurring as a result of car accidents, represents a significant public health concern. However, to date, no comprehensive study has utilized bibliometric approaches to analyze all published research on whiplash injury. Therefore, our study aims to provide an overview of current trends and the global research landscape using bibliometrics and visualization software. We performed a bibliometric analysis of the data retrieved and extracted from the Web of Science Core Collection database in whiplash injury research up to December 31, 2022. Research articles were assessed for specific characteristics, such as year of publication, country/region, institution, author, journal, field of study, references, and keywords. We identified 1751 research articles in the analysis and observed a gradual growth in the number of publications and references. The United States (379 articles, 21.64%), Canada (309 articles, 17.65%), and Australia (280 articles, 16.00%) emerged as the top-contributing countries/regions. Among institutions, the University of Queensland (169 articles, 9.65%) and the University of Alberta (106 articles, 6.05%) demonstrated the highest productivity. "Whiplash," "Neck Pain," "Cervical Spine Disease," and "Whiplash-associated Disorders" are high-frequency keywords. Furthermore, emerging areas of research interest included traumatic brain injury and mental health issues following whiplash injury. The number of papers and citations has increased significantly over the past 2 decades. Whiplash injury research is characteristically multidisciplinary in approach, involving the fields of rehabilitation, neuroscience, and spinal disciplines. By identifying current research trends, our study offers valuable insights to guide future research endeavors 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.072 | 0.089 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".