Numerical analysis of a poroelastic cartilage model: Investigating the influence of changing material properties in osteoarthritis
Bibliographic record
Abstract
Several changes occur in both the cartilage's material properties and anatomical structure as osteoarthritis progresses. Unlike most numerical studies that solely consider individual changes, our study aimed to understand the impact on cartilage mechanics by considering the combined effect of material properties and cartilage thickness varied with osteoarthritis progression. In total, 3 three-dimensional finite element models, representing the intact, early, and late osteoarthritis conditions, were developed to simulate a load-bearing area in the knee. The articular cartilage was modelled as fluid-saturated linear biphasic poroelastic to incorporate solid-fluid interaction. All models underwent prolonged creep (50 N) and relaxation (0.3 mm) analyses for 600 s. In the early stage of osteoarthritis, the tibial cartilage demonstrated an overall stiffer behaviour attributed to cartilage swelling despite decreased stiffness at the material level. On the other hand, in the late stage of osteoarthritis, the decrease in cartilage thickness led to increased knee deformation. Additionally, increased permeability resulted in accelerated fluid exudation across all osteoarthritis models, and the elevation in void ratio further intensified fluid pressure within the cartilage to a higher magnitude. Furthermore, these changes collectively influenced both the magnitude and distribution of the outcomes. A holistic understanding of the material properties altered in osteoarthritis may contribute to a better understanding of the mechanical performance of cartilage during disease progression.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".