Citation analysis of the most influential publications on whiplash injury: A STROBE-compliant study
Bibliographic record
Abstract
Whiplash injury is a common diagnosis and causes substantial economic burden. Numerous papers have been published to provide new insights into whiplash injury. However, so far there has not been a comprehensive analysis of the most influential publications on whiplash injury. This study aimed to determine the 100 most cited publications on whiplash injury and analyze their characteristics. A keyword search was conducted using the Web of Science database. The top 100 cited publications relevant to whiplash injury were gathered. The main characteristics including title, year of publication, citation, authorship, journal, country, institution, and topic were generated. The number of citations of the top 100 cited publications ranged from 82 to 777. Fifteen countries contributed the top 100 publications. Australia had the largest number of publications (26), followed by the United States (21), and Canada (12). The majority of the publications were from Europe (40) and North America (33). A total of 19 institutions and 17 authors published more than one publication. The University of Queensland (16) and the author Sterling M (7) had the leading publication record. This is the first citation analysis to identify and characterize the highest impact researches on whiplash injury. The present analysis provides the most influential studies on whiplash injury, and reveals the leading journals, counties, institutions, and authors with special contributions in this filed. The list may serve as an archive of historical development of whiplash injury and a basis for further research.
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 | 0.085 | 0.320 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.066 | 0.078 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".