Assessment of the elastic properties of human vertebral trabecular bone using computational mechanical tests and x-ray microtomography—a subvolume analysis
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Bibliographic record
Abstract
Abstract Trabecular bone structures can be modeled as a linear elastic solid, with a heterogeneous and anisotropic structure. The HR-pQCT technique is ideal for the characterization of trabecular bone to measure aspects of bone quality in diseases such as osteoporosis. In this investigation, twelve human vertebrae were used for the investigation of the mechanical properties of trabecular bone by finite element analysis (FEA). A virtual cube sample with 18.5 mm sides was extracted from each vertebrae and four smaller central cubes were obtained from it, with a 20% reduction of volume for each cube. The direct mechanics approach by FEA was performed (FAIM v6.0, Numerics88 Solutions Ltd) and mean values on three mean directions of loading resulting in: E 1 = 294 MPa, E 2 = 258 MPa, E 3 = 153 MPa, G 23 = 86 MPa, G 31 = 103 MPa, G 12 = 100 MPa. The Statistical Analysis was applied showing that E 1 values are statically different from E 3 , and E 2 are statically different from E 3 , with E 2 equal to E 1 . This indicates that there are two different mean directions of loading on these trabecular bone samples of human vertebrae. The assessment of microstructural properties showed a tendency to increased connectivity of trabeculae, which occurs as the reduction of the analyzed subvolumes (100% to 20% or 18.5 mm to 3.7 mm) followed by an addition of bone volume fraction values. Those results highlight the idea that mechanical properties are better described in local regions, in other words, a local assessment with smaller sample size maintain the volume fraction and connectivity improving the prediction of bone strength. The mechanical properties are better associated with microstructural information in the subvolume, reducing the time of scan and radiation dose, which can generate bone quality parameters, for the diagnosis of bone diseases and prediction of fracture risk of bone structures with higher accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it