Spinal injury analysis for typical snowboarding backward falls
Bibliographic record
Abstract
Spinal injury (SPI) often causes death and disability in snow-sport accidents. SPIs often result from spinal compression and flexion, but the injury risks due to over flexion have not been studied. Back protectors are used to prevent SPIs but the testing standards do not evaluate the flexion-extension resistance. To investigate SPI risks and to better define back-protector specifications, this study quantified the flexion-extension range of motions (ROMs) of the thoracic-lumbar spine during typical snowboarding backward falls. A human facet-multibody model, which was calibrated against spinal flexion-extension responses and validated against vehicle-pedestrian impact and snowboarding backward fall, was used to reproduce typical snowboarding backward falls considering various initial conditions (initial velocity, slope steepness, body posture, angle of approach, anthropometry, and snow stiffness). The SPI risks were quantified by normalizing the numerical spinal flexion-extension ROMs against the corresponding ROM thresholds from literature. A high risk of SPI was found in most of the 324 accident scenarios. The thoracic segment T6-T7 had the highest injury risk and incidence. The thoracic spine was found more vulnerable than the lumbar spine. Larger anthropometries and higher initial velocities tended to increase SPI risks while bigger angles of approach helped to reduce the risks. SPIs can result from excessive spinal flexion-extension during snowboarding backward falls. Additional evaluation of back protector's flexion-extension resistance should be included in current testing standards. An ideal back protector should consider the vulnerable spinal segments, the snowboarder's skill level and anthropometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".