Numerical Investigation of Intra-abdominal Pressure Effects on Spinal Loads and Load-Sharing in Forward Flexion
Bibliographic record
Abstract
The intra-abdominal pressure (IAP), which generates extensor torque and unloads the spine, is often neglected in most of the numerical studies that use musculoskeletal (MSK) or finite element (FE) spine models. Hence, the spinal loads predicted by these models may not be realistic. In this work, we quantified the effects of IAP variation in forward flexion on spinal loads and load-sharing using a novel computational tool that combines a MSK model of the trunk with a FE model of the ligamentous lumbosacral spine. The MSK model predicted the trunk muscle and reaction forces at the T12-L1 junction, with or without the IAP, which served as input in the FE model to investigate the effects of IAP on spinal loads and load-sharing. The findings confirm the unloading role of the IAP, especially at large flexion angles. Inclusion of the IAP reduced global muscle forces and disc loads, as well as the intradiscal pressure (IDP). The reduction in disc loads was compensated for by an increase in ligament forces. The IDP, as well as the strain of the annular fibers were more sensitive to the IAP at the upper levels of the spine. Including the IAP also increased the ligaments' load-sharing which reduced the role of the disc in resisting internal forces. These results are valuable for more accurate spinal computational studies, particularly toward clinical applications as well as the design of disc implants.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".