Focal cartilage defect compromises fluid‐pressure dependent load support in the knee joint
Bibliographic record
Abstract
A focal cartilage defect involves tissue loss or rupture. Altered mechanics in the affected joint may play an essential role in the onset and progression of osteoarthritis. The objective of the present study was to determine the compromised load support in the human knee joint during defect progression from the cartilage surface to the cartilage-bone interface. Ten normal and defect cases were simulated with a previously tested 3D finite element model of the knee. The focal defects were considered in both condyles within high load-bearing regions. Fluid pressurization, anisotropic fibril-reinforcement, and depth-dependent mechanical properties were considered for the articular cartilages and menisci. The results showed that a small cartilage defect could cause 25% reduction in the load support of the knee joint due to a reduced capacity of fluid pressurization in the defect cartilage. A partial-thickness defect could cause a fluid pressure decrease or increase in the remaining underlying cartilage depending on the defect depth. A cartilage defect also increased the shear strain at the cartilage-bone interface, which was more significant with a full-thickness defect. The effect of cartilage defect on the fluid pressurization also depended on the defect sites and contact conditions. In conclusion, a focal cartilage defect causes a fluid-pressure dependent load reallocation and a compromised load support in the joint, which depend on the defect depth, site, and contact condition.
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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 | 0.000 | 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".