A Cadaverically Evaluated Dynamic FEM Model of Closed-Chain TKR Mechanics
Bibliographic record
Abstract
Knowledge of the behavior and mechanics of a total knee replacement (TKR) in an in vivo environment is key to optimizing the functional outcomes of the implant procedure. Computational modeling has shown to be an important tool for investigating biomechanical variables that are difficult to address experimentally. To assist in examining TKR mechanics, a dynamic finite-element model of a TKR is presented. The objective of the study was to develop and evaluate a model that could simulate full knee motion using a physiologically consistent quadriceps action, without prescribed joint kinematics. The model included tibiofemoral (TFJs) and patellofemoral joints (PFJs), six major ligament bundles and was driven by a uni-axial representation of a quadricep muscle. An initial parameter screening analysis was performed to assess the relative importance of 31 different model parameters. This analysis showed that ligament insertion location and initial ligament strain were significant factors affecting simulated joint kinematics and loading, with the contact friction coefficient playing a lesser role and ligament stiffness having little effect. The model was then used to simulate in vitro experiments utilizing a flexed-knee-stance testing rig. General model performance was assessed by comparing simulation results with experimentally measured kinematics and tibial reaction forces collected from two implanted specimens. The simulations were able to reproduce experimental differences observed between the test specimens and were able to accurately predict trends seen in the tibial reaction loads. The simulated kinematics of the TFJ and PFJ were less consistent when compared with experimental data but still reproduced many trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".