Joint contact mechanics in the early stages of osteoarthritis
Bibliographic record
Abstract
Joint degeneration in the early stages of osteoarthritis (OA) may be reflected in changes in structural and material properties in articular cartilage. The aim of the present study was to simulate numerically the contact area and stress distribution in normal and "diseased" cartilage layers for dynamic loading. The initial stages of osteoarthritis were simulated based on an experimental model: the anterior cruciate ligament-transected cat knee. In this model, cartilage layers become thicker, softer, and more permeable than the corresponding healthy cartilage layers within weeks of intervention. In our numerical simulations, the diseased cartilage was modelled by changing the thickness, permeability, shear modulus, and Poisson's ratio of the cartilage in accordance with observations in this experimental model of osteoarthritis. The theoretical model of normal and diseased articular cartilage was based on a biphasic representation of cartilage, and the joint was assumed to be axi-symmetric. It was found that, for a given loading condition, the contact areas increase and peak stresses decrease in the diseased compared to the normal joint. According to our simulations, areas of normal joint contact become unloaded and areas of little or no contact become overloaded in the early stages of osteoarthritis compared to the situation in normal joints. Based on these results, we speculate that OA may be initiated following ACL transection because of an overloading of specific regions of the joint, either because of the altered contact mechanics or the disrupted joint stability, despite a general decrease in the contact pressure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".