Investigation of Motorcyclist Cervical Spine Trauma Using HUMOS Model
Bibliographic record
Abstract
OBJECTIVE: With 16 percent of the total road user fatalities, motorcyclists represent the second highest rate of road fatalities in France after car occupants. Regarding road accidents, a large proportion of trauma was on the lower cervical spine. According to different clinical studies, it is postulated that the cervical spine fragility areas are located on the upper and lower cervical spine. In motorcycle crashes, impact conditions occur on the head segment with various orientations and impact directions, leading to a combination of rotations and compression. Hence, motorcyclist vulnerability was investigated considering many impact conditions. METHOD: Using the human model for safety (HUMOS), a finite element model, this work aims to provide an evaluation of the cervical spine weaknesses based on an evaluation of injury mechanisms. This evaluation consisted of defining 2 injury risk factors (joint injury and bone fracture) using a design of experiment including various velocities, impact directions, and impact orientations. RESULTS: The results confirmed previously reported clinical and epidemiological work on the fragility of the lower cervical spine and the upper cervical spine segments. Joint injuries appeared before bone fractures on both the upper and lower cervical spine. Bone fracture risk was greater on the lower cervical spine than on the upper cervical spine. The compression induced by a high impact angle was identified as an important injury severity factor. It significantly increased the injury incidence for both joint injuries and bone fractures. It also induced a shift in injury location from the lower to the upper cervical spine. The impact velocity exhibited a linear relationship with injury risks and severity. It also shifted the bone fracture risk from the lower to upper spinal segments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".