Effect of changes in cruciate ligaments pretensions on knee joint laxity and ligament forces
Bibliographic record
Abstract
The knee joint cruciate ligaments are reconstructed with the rationale to avoid joint instability, recurrent injury, damage to soft tissues and osteoarthritis. Wide range of procedures with different stiffness, pretension, orientation and insertion locations have been proposed with the primary goal to restore the joint laxity. Apart from the general lack of success in preservation of force in the reconstructed ligament itself, the concern, not yet addressed, arises as to the effect of such perturbation on the other intact cruciate ligament. The interaction between cruciate ligament forces is hypothesized in this work. Using a 3-D nonlinear finite element model of the tibiofemoral joint, we examined this hypothesis by quantifying the extent of coupling between cruciate ligaments while varying the prestrain in each ligament under flexion with and without anterior-posterior (A-P) loads. A remarkable coupling was predicted between cruciate ligament forces in flexion thus confirming the hypothesis; forces in both cruciate ligaments increased as initial strain or pretension in one of them increased whereas they both diminished as one of them became slack. Moreover, changes in laxity and in ligament forces as a cruciate ligament prestrained or pretensioned varied with flexion angle and external loads. These findings have important consequences in joint functional biomechanics following a ligament injury or replacement surgery and in selection of laxity matched or ligament force matched pretensioning protocols.
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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".