Effect of Coupled Rotations on Knee Joint Ligament Forces Under Drawer Loads in Flexion
Bibliographic record
Abstract
A validated non-linear 3-D finite element model of human tibiofemoral joint was utilized to investigate the effect of constraint on tibial coupled internal-external and varus-valgus rotations on the passive joint response and force in ligaments under 100N drawer loads at different flexion angles. The model consisted of two bony structures and their articular cartilage layers, menisci and four principal ligaments. For the cruciate ligaments, the results showed that, in the fully unconstrained joint, ACL force decreased with flexion but remained as the primary ligament to resist the posterior femoral load throughout the range of flexion considered. A further significant decrease in ACL force with flexion angle was computed as the joint coupled rotations were constrained. As for PCL ligament, a minor contribution was at full extension under 100N anterior femoral load which further decreased as the coupled rotations were constrained. With joint flexion up to 90°, PCL force, however in contrast to ACL force, substantially increased in both constrained and unconstrained joints. Collateral ligaments, in the unconstrained joint at full extension, were the primary structures to resist the anterior femoral load but had negligible role in posterior-directed load. With joint flexion up to 90°, however, forces in collateral ligaments diminished. Similar trends were computed after fixing coupled tibial rotations with the exception of much greater LCL force and smaller MCL force at full extension under femoral anterior load and larger MCL force in flexion.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".