Severe street and mountain bicycling injuries in adults: a comparison of the incidence, risk factors and injury patterns over 14 years
Bibliographic record
Abstract
BACKGROUND: Street and mountain bicycling are popular recreational activities and prevalent modes of transportation with the potential for severe injury. The purpose of this investigation was to compare the incidence, risk factors and injury patterns among adults with severe street versus mountain bicycling injuries. METHODS: We conducted a retrospective cohort study using the Southern Alberta Trauma Database of all adults who were severely injured (injury severity score [ISS] ≥ 12) while street or mountain bicycling between Apr. 1, 1995, and Mar. 31, 2009. RESULTS: Among 11 772 severely injured patients, 258 (2.2%) were injured (mean ISS 17, hospital stay 6 d, mortality 7%) while street (n = 209) or mountain bicycling (n = 49). Street cyclists were often injured after being struck by a motor vehicle, whereas mountain bikers were frequently injured after faulty jump attempts, bike tricks and falls (cliffs, roadsides, embankments). Mountain cyclists were admitted more often on weekends than weekdays (61.2% v. 45.0%, p = 0.040). Injury patterns were similar for both cohorts (all p > 0.05), with trauma to the head (67.4%), extremities (38.4%), chest (34.1%), face (26.0%) and abdomen (10.1%) being common. Spinal injuries, however, were more frequent among mountain cyclists (65.3% v. 41.1%, p = 0.003). Surgical intervention was required in 33.3% of patients (9.7% open reduction internal fixation, 7.8% spinal fixation, 7.0% craniotomy, 5.8% facial repair and 2.7% laparotomy). CONCLUSION: With the exception of spine injuries, severely injured cyclists display similar patterns of injury and comparable outcomes, regardless of style (street v. mountain). Helmets and thoracic protection should be advocated for injury prevention.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".