A three-dimensional multibody computational model of lumbar spine
Bibliographic record
Abstract
The lower back is the most sensitive part of the human spine and over loading and bad posture during lifting can damage this area of the body. The lumbar spine consists of five vertebrae, which are responsible for carrying the weight of the upper body and loads. Intervertebral discs allow articulation between vertebrae. These discs are primarily made of non-homogeneous soft tissue, which allows the vertebrae to move and flex in all directions. Biomechanical models have been developed in the past decades to model and to predict the behavior of the spine in response to different loads. With the advances in computer modeling technology, analytical methods have become more popular in modeling the spine. These models are more cost effective and practical compared to the early models and use of human volunteers and cadavers. Unfortunately due to the complexity of the spine, most of the models failed to offer an accurate estimation of reaction moments and forces. Most models also use proprietary and custom-made software which makes it difficult for other researchers to use and modify them. This thesis reports the development and verification of a multi-body computational model of the lumbar spine. The model comprises five lumbar vertebrae (L1 to L5) and pelvis (S1). The vertebrae are connected to each other by invertebral discs, which consist of an anatomically correct kinematic and dynamic constraints. This combination represents a six degree-of-freedom mobility and enables the model to accommodate flexion, lateral bending, and axial rotation. The model is validated by carrying out a series of case studies including experimental motion studies. It is also used for preliminary evaluation of an ergonomical device called the dynamic trunk support (DTS), developed at Ryerson, School of Occupational and Public Health, in conjunction with the Mechanical and Industrial Engineering department. The results are in good agreement with the experimental results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".