Computational Biomechanics of Human Spine Under Wrapping Compression Loading
Bibliographic record
Abstract
Abstract Computational biomechanics of the human spine under a novel compression loading that follows the curvature of the spine is performed by evaluation and comparison of the detailed response of the spine under various types of compression loading at different postures. The nonlinear finite element formulation of wrapping elements sliding without friction over solid body edges is developed and used to study the load-bearing capacity of thoracolumbar (T1-S1) and lumbosacral (L1-S1) spines under one or several wrapping compression forces. Follower load at the L1, axially-fixed compression at the L1, and combined axially-fixed compression plus constrained rotations are also considered for comparison. Moreover, for the detailed lumbosacral model, the effect of changes in the position of wrapping elements and in the lumbar curvature on results are considered. The idealized wrapping loading substantially stiffens the spine allowing it to carry very large compression loads without hypermobility. It diminishes local segmental shear forces and moments as well as tissue stresses. In comparison to fixed axial compression, therefore, the compression loading by wrapping elements that follow the spinal curvatures increases the load-bearing capacity in compression and provides a greater margin of safety against both instability and tissue injury. These findings suggest a plausible mechanism in which postural changes and muscle activation patterns could be exploited to yield a loading configuration similar to that of the wrapping loading. To alleviate hypermobility in compression, the wrapping loading could also allow for the application of meaningful compression loads in experimental as well as model studies of the multi-segmental spinal biomechanics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".