A Numerical Model of Mechanics of Osteoarthritis in Human Knee Joint
Bibliographic record
Abstract
Articular cartilage is composed of water entrapped in a solid matrix formed by proteoglycans and collagen fibers. Therefore, the mechanical behavior of this tissue is determined by all of these three components. In addition, the properties of articular cartilage vary along the depth and by location. In the human knee joint, the three dimensional geometry as well as the contact between the cartilaginous tissues plays essential roles in the joint mechanics. On the other hand, initiation and progression of osteoarthritis (OA) could be partly caused by contact loads. Consequently, the fibrillar and non-fibrillar matrices, the three dimensional geometry and the contact between the tissues should be considered as essential parameters in the study of the mechanics of osteoarthritis. However, previous studies on OA mechanics were mostly limited to explants geometries [1]. Also, the contact mechanics associated with the fluid pressure have not been considered in the previous OA models. In a recent knee model, fluid was considered in femoral cartilage but not in the menisci [2]. Additionally, the depth-dependent mechanical properties were not included in that model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".