Bibliographic record
Abstract
OBJECTIVE: To critically examine the literature on skiing and snowboarding injuries in children and adolescents. DATA SOURCES: Searched English language articles from: Medline, SPORTDiscus, Cumulative Index to Nursing and Allied Health Literature, Current Contents, and HealthSTAR. The table of contents for Ski Trauma and Skiing Safety Series published by the American Society for Testing and Materials were also examined. MeSH headings included: Sports, Athletic Injuries, and Accidents. Keywords used within these headings were Skiing and/or Snowboarding with focus on children, adolescents, youth, students, or age group-related comparisons. MAIN RESULTS: The patterns and rates of injury differed markedly by activity and study design. Most studies were case-series investigations providing little useful information on risk factors. Intrinsic risk factors included: lower ability, younger age, past injury, and female sex. Extrinsic risk factors were improper binding adjustment, no helmet, certain slope characteristics, and no wrist guards. The literature on the effect of activity, equipment ownership and lessons on injury risk was equivocal. CONCLUSIONS: Suggestions for injury prevention include the use of helmets and wrist guards, participation on appropriate runs for ability level, proper fit and adjustment of bindings and other equipment, and taking lessons with the goal of increasing ability and learning hill etiquette. Many areas requiring further research are identified and discussed. New methodological approaches hold promise in advancing the field of ski and snowboard injury 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".