Evaluating the influence of trunk intra-muscular and intra-abdominal pressure on spinal geometric compensation
Bibliographic record
Abstract
Biomechanical modelling studies have revealed the impact of passive mechanical properties of spinal soft tissues on spinal configuration. This study extends prior work by evaluating the involvement of trunk abdominal and intramuscular pressure (IMP), on spinal geometric compensation, using a validated finite element spine model. The model included the vertebrae, rib cage, IVD, pelvis, ligaments, abdominal cavity and abdominal and spinal muscles. Over a fixed pelvis, the model underwent a 60° forward flexion. Muscles and the abdominal cavity were modelled as fluid-filled solid entities containing hydrostatic pressure elements, enabling IMP and intra-abdominal pressure (IAP) quantification. Changes in lumbar segmental rotations, spine range of motion (RoM) and curvature (thoracic kyphotic (TKA) and lumbar lordotic angle (LLA)) were analyzed following a 1) 10-fold increase and decrease in paraspinal IMP and a 2) 20-fold increase in IAP, relative to the validated model. A 10-fold increase in paraspinal IMP or 20-fold increase in IAP decreased lumbar ROM by a maximum of 8.4° and increased the TKA and LLA by a maximum of 5.8° and 4.7°, respectively during forward flexion. Heightened IAP correlated with decreased paraspinal IMP. Conversely increased paraspinal IMP correlated with IAP reductions. This investigation showed a synergistic interplay between paraspinal IMP and IAP on segmental mobility and spine geometry. The shared influence may suggest a clinical impact of targeting both muscle groups in scenarios involving lower back dysfunction.
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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) | 0.000 | 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".