Quantitative osteoporosis diagnosis of porous cancellous bone using poroelastodynamic modal analysis
Bibliographic record
Abstract
Osteoporosis is a skeletal disease characterized by reduced bone mass and microarchitectural deterioration, leading to increased fragility. This study presents a novel three-dimensional poroelastodynamic model for analyzing cancellous bone free vibration responses. The model incorporates the Navier-Stokes equations of linear elasticity and the Biot theory of porous media, allowing the investigation of osteoporosis-related changes. The analysis considers parameters like porosity, density, elasticity, Poisson ratio, and viscosity of bone marrow within the porous medium. Our findings indicate that natural frequencies of cancellous bone play a crucial role in osteoporosis prediction. By incorporating experimental data from 12 mouse femurs, we unveil insights into osteoporosis prediction. Increased porosity reduces bone stiffness, lowering natural frequencies. However, it also increases bone mass loss relative to stiffness, leading to higher frequencies. Therefore, the natural frequencies of osteoporotic bone are always higher than the natural frequencies of normal bone. Additionally, an increase in bone marrow within the pores, while increasing damping effects, also increases natural frequencies, which is another indication of osteoporosis growth in bone. The presence of bone marrow within the pores further influences natural frequencies, providing additional insights into osteoporosis growth. Thinner and smaller bones are found to be more susceptible to osteoporosis compared to larger and bigger bones due to their higher natural frequencies at equivalent porosity levels.
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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 | 0.001 | 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".