Spinal Cord Injuries Secondary to Mountain Biking Accidents — A Cause for National Alarm
Bibliographic record
Abstract
While much attention in North America has been placed on hockey and other high impact sports as causes of spinal cord injury (SCI), over the past two decades, our Level 1 trauma center has experienced a much higher number of SCI from off-road mountain biking (MTB). Here, we aimed to characterize the epidemiology of SCI secondary to MTB, and we also sought to estimate the direct and other economic costs to assess their societal impact. A retrospective review was conducted of patients with SCI from MTB who were treated at our Level 1 trauma center between 2008 and 2022. Injury details were compiled, and we calculated the associated lifetime direct and other costs. Over the 14-year period, we identified 58 individuals (average age 35.5 years, 93% male) who suffered SCI while MTB. Twenty-seven suffered motor complete SCI (14 tetraplegia, 13 paraplegia) with estimated average lifetime costs in Canadian Dollars of $4.8 M and $4.5 M each, respectively. Thirty-one suffered motor incomplete SCI (26 tetraplegia, 5 paraplegia) with estimated average lifetime costs of $2.4 M and $1.6 M each, respectively. The total estimated lifetime costs for this group of SCI individuals were $195.4 M. From 2008 to 2022, we identified an SCI from MTB accidents at a rate of 4 patients per year. Our data underscores the urgent need for increased awareness and preventive measures to reduce the incidence of these devastating injuries, particularly in regions where MTB is prevalent.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".