Video Analysis of the Biomechanics of a Bicycle Accident Resulting in Significant Facial Fractures
Bibliographic record
Abstract
INTRODUCTION: This study aimed to use video analysis techniques to determine the velocity, impact force, angle of impact, and impulse to fracture involved in a video-recorded bicycle accident resulting in facial fractures. Computed tomographic images of the resulting facial injury are presented for correlation with data and calculations. To our knowledge, such an analysis of an actual recorded trauma has not been reported in the literature. MATERIALS AND METHODS: A video recording of the accident was split into frames and analyzed using an image editing program. Measurements of velocity and angle of impact were obtained from this analysis, and the force of impact and impulse were calculated using the inverse dynamic method with connected rigid body segments. These results were then correlated with the actual fracture pattern found on computed tomographic imaging of the subject's face. RESULTS: There was an impact velocity of 6.25 m/s, impact angles of 14 and 6.3 degrees of neck extension and axial rotation, respectively, an impact force of 1910.4 N, and an impulse to fracture of 47.8 Ns. These physical parameters resulted in clinically significant bilateral mid-facial Le Fort II and III pattern fractures. DISCUSSION: These data confer further understanding of the biomechanics of bicycle-related accidents by correlating an actual clinical outcome with the kinematic and dynamic parameters involved in the accident itself and yielding a concrete evidence of the velocity, force, and impulse necessary to cause clinically significant facial trauma. These findings can aid in the design of protective equipment for bicycle riders to help avoid this type of injury.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".