Electromechanical response of articular cartilage in indentation—considerations on the determination of cartilage properties during arthroscopy
Bibliographic record
Abstract
A finite element formulation of streaming potentials in articular cartilage was incorporated into a fibril-reinforced model using the commercial software ABAQUS. This model was subsequently used to simulate interactions between an arthroscopic probe and articular cartilage in a knee joint. Fibril reinforcement was found to account for large fluid pressure at considerable strain rates, as has been observed in un-confined compression. Furthermore, specific electromechanical responses were associated with specific changes in tissue properties that occur with cartilage degeneration. For example, the strong strain-rate dependence of the load response was only observed when the collagen network was intact. Therefore, it is possible to use data measured during arthroscopy to evaluate the degree of cartilage degeneration and the source causing changed properties. However, practical problems, such as the difficulty of controlling the speed of the hand-held probe, may greatly reduce the reliability of such evaluations. The fibril-reinforced electromechanical model revealed that high-speed transient responses were associated with the collagen network, and equilibrium response was primarily determined by proteoglycan matrix. The results presented here may be useful in the application of arthroscopic tools for evaluating cartilage degeneration, for the proper interpretation of data, and for the optimization of data collection during arthroscopy.
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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) | 0.000 | 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 | 0.001 |
| 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".