Boarder belly: Splenic injuries resulting from ski and snowboarding accidents
Bibliographic record
Abstract
OBJECTIVE: Snowboarding has increased in popularity worldwide, with an associated increase in injuries suffered by its participants with a significant proportion of these injuries being severe. We sought to understand the risk of sustaining a splenic injury in snowboarders as compared to skiers, and whether there are noteworthy differences in their characteristics at hospital admission. METHODS: A 10-year retrospective review was conducted on patients with splenic injury resulting from snowboarding or skiing, who were admitted to the principle ED and referral hospital servicing several busy downhill skiing areas. Population-based injury rates were calculated for our catchment area, using data provided by the Canadian Ski Council. RESULTS: Controlling for gender, snowboarders were six times more likely to sustain a splenic injury than skiers (P < 0.0001). The risk of splenic injury was 21.7 times greater for male snowboarders than for female snowboarders (P = 0.002). By contrast, no gender differences were observed for skiers. Snowboarders admitted to hospital with a splenic injury were significantly younger, more likely to present with an isolated injury and to required a shorter hospital stay, as compared to skiers. CONCLUSION: The risk of sustaining an injury of the spleen resulting from blunt abdominal trauma while snowboarding is significantly greater than the risk while downhill skiing. Male snowboarders have a significantly higher risk of splenic injury than female snowboarders. In the majority of cases, snowboarders sustained their injuries as a result of falls or jumps.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".