Investigation of Lumbosacral Spine Anatomical Variation Effect on Load-Partitioning Under Follower Load Using Geometrically Personalized Finite Element Model
Bibliographic record
Abstract
The spinal load sharing and mechanical stresses developed in the spine segments due to mechanical loads are dependent on the unique spinal anatomy (geometry and posture). Variation in spinal curvature alters the load sharing of the lumbar spine as well as the stiffness and stability of the passive tissues. In this paper, effects of lumbar spine curvature variation on spinal load sharing under compressive Follower Load (FL) are investigated numerically. 3D nonlinear Finite Element (FE) models of three ligamentous lumbosacral spines are developed based on personalized geometries; hypo-lordotic (Hypo-L), normal (Normal-L) and hyper-lordotic (Hyper-L) cases. Analysis of each model is performed under Follower Load and developed stress in the discs and forces in the collagen fibers are investigated. Stresses on the discs vary in magnitude and distribution depending on the degree of lordosis. A straight hypo-lordotic spine shows stresses more equally distributed among discs while a highly curved hyper-lordotic spine has stresses concentrated at lower discs. Stresses are uniformly distributed in each disc for Hypo-L case while they are concentrated posteriorly for Hyper-L case. Also, the maximum force in collagen fibers is developed in the Hyper-L case. These differences might be clinically significant related to back pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".