A neck compression injury criterion incorporating lateral eccentricity
Bibliographic record
Abstract
There is currently no established injury criterion for the spine in compression with lateral load components despite this load combination commonly contributing to spinal injuries in rollover vehicle crashes, falls and sports. This study aimed to determine an injury criterion and accompanying tolerance values for cervical spine segments in axial compression applied with varying coronal plane eccentricity. Thirty-three human cadaveric functional spinal units were subjected to axial compression at three magnitudes of lateral eccentricity of the applied force. Injury was identified by high-speed video and graded by spine surgeons. Linear regression was used to define neck injury tolerance values based on a criterion incorporating coronal plane loads accounting for specimen sex, age, size and bone density. Larger coronal plane eccentricity at injury was associated with smaller resultant coronal plane force. The level of coronal plane eccentricity at failure appears to distinguish between the types of injuries sustained, with hard tissue structure injuries more common at low levels of eccentricity and soft tissue structure injuries more common at high levels of eccentricity. There was no relationship between axial force and lateral bending moment at injury which has been previously proposed as an injury criterion. These results provide the foundation for designing and evaluating strategies and devices for preventing severe spinal injuries.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".