Spinal Column and Spinal Cord Injuries in Mountain Bikers
Bibliographic record
Abstract
BACKGROUND: Multiple studies have described in general the injuries associated with mountain biking, and detailed accounts of spine injuries sustained in hockey, gymnastics, skiing, snowboarding, rugby, and paragliding have previously been published. However, no large-scale detailed assessment of mountain biking associated spinal fractures and spinal cord injuries has previously been published. PURPOSE: This study was undertaken to describe the patient demographics, injuries, mechanisms, treatments, outcomes, and resource requirements associated with spine injuries sustained while mountain biking. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Patients who were injured while mountain biking, and who were seen at a provincial spine referral center between 1995 and 2007 inclusive, with spinal cord injuries and/or spine fracture were included. A chart review was performed to obtain demographic data, and details of the injury, treatment, outcome, and resource requirements. RESULTS: A total of 102 men and 5 women were identified for inclusion. The mean age at injury was 32.7 years (95% confidence interval 30.6, 35.0). Seventy-nine patients (73.8%) sustained cervical injuries, while the remainder sustained thoracic or lumbar injuries. Forty-three patients (40.2%) sustained a spinal cord injury. Of those with cord injuries, 18 (41.9%) were American Spinal Injury Association (ASIA) A, 5 (11.6%) were ASIA B, 10 (23.3%) ASIA C, and 10 (23.3%) ASIA D. Sixty-seven patients (62.6%) required surgical treatment. The mean length of stay in an acute hospital bed was 16.9 days (95% confidence interval 13.1, 30.0). Thirty-three patients (30.8%) required intensive care unit attention, and 31 patients (29.0%) required inpatient rehabilitation. Of the 43 patients (40.2%) seen with spinal cord injuries, 14 (32.5%) improved by 1 ASIA category, and 1 (2.3%) improved by 2 ASIA categories. Two patients remained ventilator-dependent at discharge. CONCLUSION: Spine fractures and spinal cord injuries caused by mountain biking accidents typically affect young, male, recreational riders. The medical, personal, and societal costs of these injuries are high. Injury prevention should remain a primary goal, and further research is necessary to explore the utility of educational programs, and the effect of helmets and other protective gear on spine injuries sustained while mountain biking.
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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 | 0.001 | 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) | 0.004 | 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".