Design Optimization of a Total Hip Prosthesis for Wear Reduction
Bibliographic record
Abstract
Aseptic loosening from polyethylene debris is the leading cause of failure for metal-on-polyethylene hip implants. The accumulation of wear debris can lead to osteolysis, the degradation of bone surrounding the implant components. In the present study, a parametric three-dimensional finite element model of an uncemented total hip replacement prosthesis was constructed and implanted into a femur model constructed from computed tomography (CT) scan data. Design optimization was performed considering volumetric wear as an objective function using a computational model validated in a previous study through in vitro wear assessment. Constraints were used to maintain the physiological range of motion of wear-optimum designs. Loading conditions for both walking and stair climbing were considered in the analysis. In addition, modification of the acetabular liner surface nodes was performed in discrete intervals to reflect the actual wear and creep damage occurring on the liner surface. Stair climbing was found to produce 49% higher volumetric wear than walking. Using a sensitivity analysis, it was found that the objective function sensitivity to the chosen design variables was identical for both walking and stair climbing. The greatest reduction in volumetric wear achieved while maintaining a physiological range of motion was 16%. It was found that including nodal modification in the sensitivity analysis produced little or no difference in the sensitivity analysis results due to the linear nature of volumetric wear progression. Thus, nodal modification was not used in optimization. An increase in the maximum contact pressure was observed for all wear-optimized designs, and an increase in head-liner penetration was found to be related to a reduction in volumetric 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".