Injury patterns of equine-related trauma: A fifteen-year review of hospital admissions to a level 1 trauma center
Bibliographic record
Abstract
OBJECTIVE: Little information exists about horse-related injury admissions to Level 1 trauma centers in the Western United States. This study describes injury patterns in this population, to reveal potential areas for injury prevention initiatives. METHODS: A retrospective database review of 512 non-fatal equine-related injuries over a 15-year period was conducted, using a Level 1 hospital trauma registry. To determine patterns of injury, patients injured by riding or being near a horse were classified according to age, sex, helmet use, abbreviated injury score, anatomical region injured, and length of stay. RESULTS: Equine-related injury was more frequent among females than males, the anatomical region most at risk among adults was the lower extremity (including pelvis), and among children and youth, the head. 75% of head-injured patients were not wearing a helmet at the time of injury and those with the most severe head injuries were least likely to be wearing a helmet. CONCLUSION: Preventable equine-related injuries occur across all ages, are more frequent among females, and affect all regions of the body. Despite head-injury risks associated with horse activities, helmet use was not common among most of the injured. Decreasing the risk of these injuries requires use of appropriate protective equipment and enhanced education campaigns aimed at those in the horse industry and the general public.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".