Reduced stress shielding with limited micromotions using a carbon fibre composite biomimetic hip stem: a finite element model
Bibliographic record
Abstract
Total hip arthroplasty (THA) enjoys excellent rates of success in older patients, but younger patients are still at risk of aseptic loosening and bone resorption from stress shielding. One solution to the stress shielding problem is to use a hip stem with mechanical properties matching those of cortical bone. The objective of the present study was to investigate numerically the biomechanical performance of such a biomimetic hip stem based on a hydroxyapatite (HA)-coated carbon fibre composite. A finite element model (FEM) of the biomimetic stem was constructed. Contact elements were studied to model the bone-implant interface in a non-osseointegrated and osseointegrated state in the best way. Three static load cases representing slow walking, stair climbing, and gait in a healthy individual were considered. Stress shielding and bone-implant interface micromotions were evaluated and compared with the results of a similar FEM based on titanium alloy (Ti-6Al-4V). The composite stems allowed for reduced stress shielding when compared with a traditional Ti-6Al-4V stem. Micromotions were slightly higher with the composite stem, but remained below 40 microm on most of the HA-coated surface. It is concluded that a biomimetic composite stem might offer a better compromise between stress shielding and micromotions than the Ti-6Al-4V stem with the same external geometry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".