The Effect of Static Torsion on the Compressive Strength of the Spine
Bibliographic record
Abstract
STUDY DESIGN: Matched porcine cervical spine motion segments were subjected to two main conditions and compared: axial compression and axial compression combined with varying axial torque. OBJECTIVES: To determine the effect of torsion on the acute compressive strength of the spine. SUMMARY OF BACKGROUND DATA: The spine is often subjected to compression together with axial torque as a component of complex loading, yet there is a lack of documentation on its effect on the compressive strength and injury mechanics. METHODS: Matched cohorts of porcine cervical spine (C5-C6) motion segments were compressed to failure at a rate of 3,000 N/s combined with 0 Nm, 5 Nm, 20 Nm, or 30 Nm of axial torque. Three "failure" points were recorded from the stress/strain association: the first "step" (initial microfracture), the initial slope change (yield point or "slow crush" mechanism), and the ultimate failure point (fracture). Furthermore, resultant injuries were documented using planar radiography and visual inspection following dissection of the motion segments. RESULTS: Axial torque affected the failure characteristics during acute compressive loading. The ultimate strength of the motion segments was significantly reduced with increasing static torques. The compressive load at which initial microfracture occurred, indicated by the first "step" in the load-deformation curve, was increased with 5 Nm, 10 Nm, and 20 Nm of applied torsion in comparison to no torque, but this effect was reduced with 30 Nm of torque. The "slow crush" mechanism of failure was not affected by the addition of axial torque. No radiographic gross injuries to the facet joints were observed. Damage appeared to be confined to the endplate and trabecular network of the vertebral body. CONCLUSIONS: Based on this animal model, shown to have similar biomechanical behavior to humans, axial torque appears to significantly reduce the compressive strength of the spine.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".