The most cited publications on snowboarding-related head injuries, concussions, and injury distribution
Bibliographic record
Abstract
Objectives The purpose of this study was to identify the 50 most-cited publications relating to snowboarding and conduct a bibliometric analysis of the identified studies.Methods Clarivate Analytics Web of Science database was queried to identify all publication titles, abstracts, and keywords related to snowboarding. The resulting articles were sorted by total number of citations. Titles and abstracts were included based on their relevance to snowboarding. Once the 50 most cited articles were identified, each article was further analyzed to obtain author name, publication year, country of origin, journal name, article type, research topic, competition level, total number of citations, and the level of evidence. Citation density (total number of citations/years since publication) was calculated and recorded for each of the most-cited studies.Results The 50 most-cited articles were cited 4,123 times with an average of 82.5 citations per article. The most cited article was cited 212 times. The majority of articles came from 2 different countries, with the United States and Canada contributing 18 and 10 articles, respectively. The American Journal of Sports Medicine published the most articles (n = 11). The most studied topic was injury distribution (n = 25) followed by head injuries (n = 8). Recreational snowboarding was the most studied level of competition (n = 43).Conclusions The 50 most-cited articles related to snowboarding are predominantly cohort and review studies from the United States and Canada, focusing on recreational athletes. These articles primarily detail the total anatomic distribution of snowboard-related injuries, particularly head injuries and concussions.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.073 | 0.059 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".