A Bibliometric Analysis of the Top Cited Articles in Sports and Exercise Medicine
Bibliographic record
Abstract
BACKGROUND: Although citation analysis is common in many areas of medicine, there is a lack of similar research in sports and exercise medicine. PURPOSE: To identify and examine the characteristics of the 100 top cited articles in the field of sports and exercise medicine in an effort to determine what components make an article highly influential. STUDY DESIGN: Cross-sectional study. METHODS: The Web of Science, Scopus, and PubMed databases were used to determine the 100 top cited articles from 46 journals in the field of sports and exercise medicine. Each of the 100 articles was then analyzed by 2 independent reviewers, and results were compared. Basic information was collected, including journal title, country of origin, and study type. Different categories were compared using descriptive statistics of counts or percentages. RESULTS: (n = 7). In terms of country of origin, the top 3 contributors were the United States (n = 65), Canada (n = 9), and Sweden (n = 8). The most commonly researched anatomic areas were the knee (n = 15) and the brain (n = 3). Narrative reviews were the most common study type (n = 38), and only a single study on the 100 top cited articles list used a randomized controlled trial design. The most prevalent fields of study were exercise science (55% of articles) and well-being (16% of articles). CONCLUSION: Narrative reviews from the United States and published in English-language journals were the most likely to be highly cited. In addition, the knee was a common anatomic area of study on the top cited list of research in sports and exercise medicine.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.016 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.195 | 0.197 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".