Customized surface-guided knee implant: Contact analysis and experimental test
Bibliographic record
Abstract
Contact pressure and stresses on the articulating surface of the tibial component of a total knee replacement are directly related to the joint contact forces and the contact area. These stresses can result in wear and fatigue damage of the ultra-high-molecular-weight polyethylene. Therefore, conducting stress analysis on a newly designed surface-guided knee implant is necessary to evaluate the design with respect to the polyethylene wear. Finite element modeling is used to analyze the design's performance in level walking, stair ascending and squatting. Two different constitutive material models have been used for the tibia component to evaluate the effect of material properties on the stress distribution. The contact pressure results of the finite element analysis are compared with the results of contact pressure using pressure-sensitive film tests. In both analyses, the average contact pressure remains below the material limits of ultra-high-molecular-weight polyethylene insert. The peak von Mises stresses in 90° of flexion and 120° of flexion (squatting) are 16.28 and 29.55 MPa, respectively. All the peak stresses are less than the fatigue failure limit of ultra-high-molecular-weight polyethylene which is 32 MPa. The average contact pressure during 90° and 120° of flexion in squatting are 5.51 and 5.46 MPa according to finite element analysis and 5.67 and 8.14 MPa according to pressure-sensitive film experiment. Surface-guided knee implants are aimed to resolve the limitations in activities of daily living after total knee replacement by providing close to normal kinematics. The proposed knee implant model provides patterns of motion much closer to the natural target, especially as the knee flexes to higher degrees during squatting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".