Prediction of backside micromotion in total knee replacements by finite element simulation
Bibliographic record
Abstract
The micromotion at the interface between the polyethylene tibial insert and metal tibial tray [corrected] in modular total knee replacements [corrected] has been shown to contribute to wear particle-induced osteolysis and may [corrected] cause implant failure. Therefore, studying the design parameters that are involved in the backside wear process is an important task that may lead to improvement in new total knee replacements. In the present study, a finite element model was developed to predict the backside micromotion along the entire modular interface. Both the linear elastic constitutive model and non-linear J2-plasticity constitutive model were considered in the finite element model for polyethylene and were corroborated against published results obtained from displacement controlled knee simulator wear tests. The finite element simulation with the non-linear J2-plasticity constitutive model was able to predict backside micromotion [corrected] more accurately than the simulation with the linear elastic constitutive model. [corrected] The developed finite element model (including the non-linear J2-plasticity constitutive model) was then applied to assess the effects of the tibial tray locking mechanism design (dovetails versus fullperipheral [corrected] design) and different levels of interference fit on insert micromotion. The developed finite element model, implementing the non-linear J2-plasticity constitutive model, was shown to successfully predict clinical amounts of backside micromotion and could be used for the design and development of total knee replacements for the reduction of backside micromotion and polyethylene [corrected] wear.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".