OPTIMISATION OF HIP STEM SHAPE AND MATERIAL PROPERTIES TO MINIMISE BONE RESORPTION AND HEAD DISPLACEMENT
Bibliographic record
Abstract
Introduction Stress shielding is one of the major concerns of load bearing implants (e.g. hip prostheses). Stiff implants cause stress shielding, which is thought to contribute to bone resorption1. On the contrary, low-stiffness implants generate high interfacial stresses that have been related to pain and interfacial micro-movements². Different attempts have been made to reduce these problems by optimizing either the stem design3 or using functionally graded implants (FGI) where the stem9s mechanical properties are optimized4. In this way, new additive manufacturing technologies allow fabricating porous materials with well-controlled mesostructure, which allows tailoring their mechanical properties. In this work, Finite Element (FE) simulations are used to develop an optimization methodology for the shape and material properties of a FGI hip stem. The resorbed bone mass fraction and the stem head displacement are used as objective functions. Methodology The 2D-geometry of a femur model (Sawbones®) with an implanted Profemur-TL stem (Wright Medical Technology Inc.) was used for FE simulations. The stem geometry was parameterized using a set of 8 variables (Figure 1-a). To optimize the stem9s material properties, a grid was generated with equally spaced points for a total of 96 points (Figure 1-b). Purely elastic materials were used for the stem and the bone. Two bone qualities were considered: good (Ecortical=20 GPa, Etrabecular=1.5 GPa) and medium (Ecortical=15 GPa, Etrabecular=1 GPa). Poisson ratio was fixed to v=0.3. Loading corresponded to stair climbing. Hip contact force along with abductors, vastus lateralis and vastus medialis muscles were considered5 for a bodyweight of 847 N. The resorbed bone mass fraction was evaluated from the differences in strain energy densities between the intact bone and the implanted bone2. The displacement of the load point on the femoral head was computed. The optimization problem was formulated as the minimization of the resorbed bone mass fraction and the head displacement. It was solved using a genetic algorithm. Results For the Profemur-TL design, bone resorption was around 36% and 56% for good and medium bone qualities, respectively (Fig. 2). The corresponding head displacements were 11.75 mm and 21.19 mm. Optimized solutions showed bone resorption from 15% to 26% and from 44% to 65% for good and medium bone qualities, respectively. Corresponding head displacements ranged from 11.85 mm to 12.25 mm and from 16.9 mm to 22.6 mm. Conclusion The obtained set of solutions constitutes an improvement of the implant performance for this functionally graded implant (FGI) compared to the original implant for both bone qualities. From these simulations, the final solution for the FGI could be chosen based on manufacturing restrictions or another performance indicator.
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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 | 0.000 |
| 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".