Computational prediction of the long-term behavior of the femoral density after THR using the Silent Hip stem
Bibliographic record
Abstract
Aseptic loosening due to the progressive periprosthetic bone resorption following total hip replacement is a crucial concern, that causes complications and failure of the arthroplasty surgery. The mismatch in stiffness between the hip implant and the surrounding femoral bone is one of the key factors leading to bone density resorption. This paper aimed to investigate the long-term response of the femoral bone after THR using the Silent Hip stem. For this purpose, a validated thermodynamic-based computational model was used to compute the change in bone density before and after THR. This model incorporated essential factors involved in bone remodeling process, such as mechanical loading, and biochemical affinities. The results of the numerical simulations using 3D finite element analysis were analyzed in five zones of interest qualitatively and quantitatively. Bone density predictions showed notable bone resorption in cervical areas, specifically in zone 1 and zone 5 of -18.7% and -14%, respectively. Conversely, bone formation was observed in the greater trochanter area (zone 2) of +25%. Stress shielding seemed to occur at cervical area due to the reduction in the mechanical loading in this region. Based on the quantitative analysis of the bone density distribution throughout the femoral bone, it appears that the Silent Hip stem achieved less bone resorption compared to conventional hip stem designs reported in the literature, which could be used for active patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".