A musculoskeletal model of the lumbar spine using ArtiSynth – development and validation
Bibliographic record
Abstract
A musculoskeletal model of the spine was created using ArtiSynth, an open-source biomechanical modelling toolkit. The model included the entire spine and rib cage, with the lumbar vertebrae being mobile and 210 muscle fascicles. Muscle parameters needed for a full Hill-type musculotendon model including tendon ratios and pennation angles along with muscle force-length and force-velocity curves were incorporated into the model, as were the nonlinear stiffness of the functional spinal units and the effect of intra-abdominal pressure. We used forward dynamics-assisted data tracking for the estimation of muscle forces and validated the solution method by comparing the predicted spinal forces vs. the results of two in vivo experiments in the literature. Our model produced larger maximum extension moment in flexion than extension, which is observed in in vivo experiments. These results could not be achieved without the inclusion of the muscle force-length relationship. The model was also able to predict the ratios of axial forces at L4–L5 as measured in vivo intradiscal pressures for three cases of upright standing, holding a crate close to and far from the chest. Due to the high stiffness of the spine, our solution method was sensitive to input kinematics, which hindered extensive validation of the model for body positions other than standing. Modifying the solution method, possibly by only tracking the angular motion of the vertebrae rather than their translational motion, should make the model less sensitive and enable further validation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".