Reconsideration on the use of elastic models to predict the instantaneous load response of the knee joint
Bibliographic record
Abstract
Fluid pressurization in articular cartilages and menisci plays an important role in the mechanical function of the knee joint. However, fluid pressure has not been incorporated in previous finite element modelling of the knee, instead elastic models of the knee are widely used. It is believed that an elastic model can be used to predict the instantaneous load response of the knee as long as large effective moduli for the cartilaginous tissues are used. In the present study, the instantaneous response of the knee was obtained from a proposed model including fluid pressure and fibril reinforcement in the cartilaginous tissues. The results were then compared with those obtained from an elastic model using the effective modulus method. It was found that the deformations and contact pressures predicted by the two models were substantially different. An unconfined compression of a tissue disc was used to help understand the issue. It was clear that a full equivalence between the instantaneous and elastic responses could not be established even for this simple case. A partial equivalence in stress could be conditionally established for a given unconfined compression, but it was not valid for a different magnitude of compression. The instantaneous deformation of the intact tissues in the joint was even more difficult to determine using the effective modulus method. The results thus obtained were further compromised because of the uncertainty over the choice of effective modulus. The tissue non-linearity was one of the factors that made it difficult to establish the equivalence in stress. The pressurized tissue behaved differently from a solid material when non-linear fibril reinforcement was presented. The direct prediction of the instantaneous response using the proposed poromechanical model had the advantage of determining the fluid pressure and incompressible deformation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".