Hemiarthroplasty implants should have very low stiffness to optimize cartilage contact stress
Bibliographic record
Abstract
Hemiarthroplasty is often preferred to total arthroplasty as it preserves native tissue; however, accelerated wear of the opposing cartilage is problematic. This is thought to be caused by the stiffness mismatch between the implant and cartilage-bone construct. Reducing the stiffness of the implant by changing the material has been hypothesized as a potential solution. This study employs a finite element model to study a concave-convex hemiarthroplasty articulation for various implant materials (cobalt-chrome, pyrolytic carbon, polyether ether ketone, ultra-high-molecular-weight polyethylene, Bionate-55D, Bionate-75D, and Bionate-80A). The effect of the radius of curvature and the degree of flexion-extension was also investigated to ensure any relationships found between materials were generalizable. The implant material had a significant effect (P < .001) for both contact area and maximum contact pressure on the cartilage surface. All of the materials were different from the native state except for Bionate-80A at two of the different flexion angles. Bionate-80A and Bionate-75D, the materials with the lowest stiffnesses, were the closest to the native state for all flexion angles and radii of curvature. No evident difference between materials occurred unless the modulus was below that of Bionate-55D (288 MPa), suggesting that hemiarthroplasty materials need to be less stiff than this material if they are to protect the opposing cartilage. This is clinically significant as the findings suggest that the development of new hemiarthroplasty implants should use materials with stiffnesses much lower than currently available devices.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".