Investigation of Muscle Recruitment Patterns in Scoliosis Using a Biomechanical Finite Element Model
Bibliographic record
Abstract
The objective of this project is to study the characteristics of trunk muscle recruitment strategies experimentally observed for scoliotic subjects using a finite element model of the trunk. The personalized biomechanical model includes elements representing the osseo-ligamentous structures of the spine, rib cage and pelvis. It also integrates the principal agonistic muscles necessary for trunk movement and a neural control model based on the Equilibrium Point hypothesis (lambda model of Feldman). Muscle recruitment patterns of normal and scoliotic subjects obtained from the simulation of lateral bending movements were qualitatively compared. The generation process of motor control variables was studied by analysing the relationships between central commands and spine segment mobility. Differences in recruitment patterns between normal and scoliotic subjects were observed, especially for paraspinal fascicles crossing the thoracic curve segment. The generation of central commands for normal subjects was strongly correlated with the amplitude of bending, but this relation was weaker for scoliotic subjects and this difference was worst at the apex vertebra. These results show that neuromuscular disorders could occur at a local level. The proposed approach should provide a simulation tool to study the multifactorial origin of scoliosis, and to investigate the implication of muscles and central commands in spinal dysfunctions.
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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".