Cervical spine posture, but not head-end motion constraints, governs the kinematic and kinetic response in sub-injurious axial impacts
Bibliographic record
Abstract
Head-first impacts can produce traumatic cervical spine injuries resulting in tetraplegia. These injury patterns are thought to relate to the alignment of the loading vector relative to the spinal column. Pre-impact posture and subsequent head and intervertebral kinematics, including spinal buckling and head motion relative to the spine and torso, can generate complex spinal configurations. These motions often precede injury onset and can be observed with ex vivo models in which applied loads remain below injury thresholds. This study examined the kinematic response of the cervical spine to dynamic axial compression at sub-injurious severities, enabling inter- and intra-specimen comparisons across varying initial spinal postures and head motion constraints. Human cervical spine specimens (N = 7) were subjected to repeated 1 m/s axial impacts, while the applied head constraint (sagittal rotation and/or anterior translation) and initial posture (anterior eccentricity and curvature) were varied. Pre-impact head-T1 eccentricity and curvature, head-end motion during impact, intervertebral kinematics, and impact loads were recorded. Head-end anterior translation and flexion rotation were minimal across all constraint conditions (<11.5 mm, <9.0°). Head constraint had no detected effect on peak force (541-2457 N), deformation (3.2-11 mm), or stiffness (81-1074 N/mm) (all p > 0.05). In contrast, greater initial curvature and eccentricity reduced stiffness and peak force, and increased deformation (p < 0.05). Greater initial curvature also produced larger changes in intervertebral flexion-extension during impact (p < 0.05). These results demonstrate that pre-impact posture dictates the cervical spine's sub-injurious axial response at discrete anterior eccentricities, which may be further explored using computational models validated using this dataset.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".