Evaluation of a Fast-Solving Rigid Body Spine Model Inclusive of Intra-Abdominal Pressure
Bibliographic record
Abstract
OBJECTIVE: Traditional spine biomechanical models often neglect the load-sharing effect of the intra-abdominal pressure (IAP) on the spine and can be computationally intensive. These limitations hinder their effectiveness in muscle recruitment simulations where iterative calculations are required. Thus, a need exists for validated fast-solving IAP-integrated musculoskeletal lumbar spine models, hence developed herein. METHODS: A rigid-body model consisting of the pelvis, lumbar vertebrae, a lumped thoracic spine and the ribcage, derived from MRI scans of a healthy adult male, was devised. The intervertebral discs were modeled as 3 degrees-of-freedom (DOF) gimbal joints using nonlinear moment-rotation relationships. Spinal ligaments were modeled as nonlinear tension-only springs. Two methods of modeling IAP were discussed and implemented. Model#1 represented IAP as normal force vectors on the diaphragm and the spine, while model#2 idealized the abdominal wall compliance using spring-damper elements inside the cavity. Level-by-level spinal stiffness was validated under pure moment loading up to 7.5 Nm in flexion-extension, lateral bending and axial rotation. RESULTS: Model segmental stiffness profiles in all three bending modes were within one standard deviation of literature datasets. IAP model #1 revealed a linear increase in the spinal extensor torque about L3 with increase in IAP, consistent with literature, while model #2 suggested decreased spinal range of motion with increased abdominal cavity stiffness. The model consisted of 15 DOFs, compiled in 6sec and simulated in 1.4sec. CONCLUSION: This MATLAB native model could be a useful tool to quickly and intuitively visualize physiological spine loading. SIGNIFICANCE: A novel fast-solving lumbar musculoskeletal model with IAP was presented in this research.
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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".