Control of Different FEM Based Musculoskeletal Models of Human Lumbar Spine Under Different Loading Conditions Using Optimization Method
Bibliographic record
Abstract
In this study the effects of using different musculoskeletal models on load-displacement behavior of FE models of the human lumbar spine under external loads and moments have been analyzed in terms of equilibrium and clinical stability. A simplified and a complex architecture of muscles have been integrated to FE based models of lumbar spine and were loaded to simulate the load carrying behavior of human lumbar spine in flexion, extension and lateral bending. The displacement values as well as muscle forces have been computed and compared in both cases using optimization methods with different cost functions. The models showed similar kinematics in pure flexion but the simplified model showed instability in flexion-lateral bending and pure lateral bending conditions. The complex model was loaded and analyzed with different cost function and it was observed that displacements in the model are lower while the angle between the load vector and spine curvature at each level is minimized. It was shown that the model is less stable in case an asymmetric loading condition is applied.
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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 | 0.000 | 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 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".