Coordination Between Trunk Muscles, Thoracolumbar Fascia, and Intra-Abdominal Pressure Toward Static Spine Stability
Bibliographic record
Abstract
STUDY DESIGN: Numerical in-silico human spine stability finite element analysis. OBJECTIVE: The purpose of this study was to investigate the contribution of major torso tissues toward static spine stability, mainly the thoracolumbar fascia (TLF), abdominal wall with its intra-abdominal pressure (IAP), and spinal muscles inclusive of their intramuscular pressure. SUMMARY OF BACKGROUND DATA: Given the numerous redundancies involved in the spine, current methodologies for assessing static spinal stability are limited to specific tissues and could lead to inconclusive results. A three-dimensional finite element model of the spine, with structured analysis of major torso tissues, allows for objective investigation of static spine stability. METHODS: A novel previously fully validated spine model was employed. Major torso tissues, mainly the muscles, TLF, and IAP were individually, and in combinations, activated under a 350N external spine perturbation. The stability contribution exerted by these tissues, or their ability to restore the spine to the unperturbed position, was assessed in different case-scenarios. RESULTS: Individual activations recorded significantly different stability contributions, with the highest being the TLF at 75%. Combined or synergistic activations showed an increase of up to 93% stability contribution when all tissues were simultaneously activated with a corresponding decrease in the tensile load exerted by the tissues themselves. CONCLUSION: This investigation demonstrated torso tissues exhibiting different roles toward static spine stability. The TLF appeared able to dissipate and absorb excessive loads, the muscles acted as antagonistic to external perturbations, and the IAP played a role limiting movement. Furthermore, the different combinations explored suggested an optimized engagement and coordination between different tissues to achieve a specific task, while minimizing individual work.Level of Evidence: N/A.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".