Knee joint passive stiffness and moment in sagittal and frontal planes markedly increase with compression
Bibliographic record
Abstract
Knee joints are subject to large compression forces in daily activities. Due to artefact moments and instability under large compression loads, biomechanical studies impose additional constraints to circumvent the compression position-dependency in response. To quantify the effect of compression on passive knee moment resistance and stiffness, two validated finite element models of the tibiofemoral (TF) joint, one refined with depth-dependent fibril-reinforced cartilage and the other less refined with homogeneous isotropic cartilage, are used. The unconstrained TF joint response in sagittal and frontal planes is investigated at different flexion angles (0°, 15°, 30° and 45°) up to 1800 N compression preloads. The compression is applied at a novel joint mechanical balance point (MBP) identified as a point at which the compression does not cause any coupled rotations in sagittal and frontal planes. The MBP of the unconstrained joint is located at the lateral plateau in small compressions and shifts medially towards the inter-compartmental area at larger compression forces. The compression force substantially increases the joint moment-bearing capacities and instantaneous angular rigidities in both frontal and sagittal planes. The varus-valgus laxities diminish with compression preloads despite concomitant substantial reductions in collateral ligament forces. While the angular rigidity would enhance the joint stability, the augmented passive moment resistance under compression preloads plays a role in supporting external moments and should as such be considered in the knee joint musculoskeletal models.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".