Effect of intervertebral translational flexibilities on estimations of trunk muscle forces, kinematics, loads, and stability
Bibliographic record
Abstract
Due to the complexity of the human spinal motion segments, the intervertebral joints are often simulated in the musculoskeletal trunk models as pivots thus allowing no translational degrees of freedom (DOFs). This work aims to investigate, for the first time, the effect of such widely used assumption on trunk muscle forces, spinal loads, kinematics, and stability during a number of static activities. To address this, the shear deformable beam elements used in our nonlinear finite element (OFE) musculoskeletal model of the trunk were either substantially stiffened in translational directions (SFE model) or replaced by hinge joints interconnected through rotational springs (HFE model). Results indicated that ignoring intervertebral translational DOFs had in general low to moderate impact on model predictions. Compared with the OFE model, the SFE and HFE models predicted generally larger L4-L5 and L5-S1 compression and shear loads, especially for tasks with greater trunk angles; differences reached ~15% for the L4-L5 compression, ~36% for the L4-L5 shear and ~18% for the L5-S1 shear loads. Such differences increased, as location of the hinge joints in the HFE model moved from the mid-disc height to either the lower or upper endplates. Stability analyses of these models for some select activities revealed small changes in predicted margin of stability. Model studies dealing exclusively with the estimation of spinal loads and/or stability may, hence with small loss of accuracy, neglect intervertebral translational DOFs at smaller trunk flexion angles for the sake of computational simplicity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".