Prediction of damage formation in hip arthroplasties by finite element analysis using computed tomography images
Bibliographic record
Abstract
Femoral bone fracture is one of the main causes for the failure of hip arthroplasties (HA). Being subjected to abrupt and high impact forces in daily activities may lead to complex loading configuration such as bending and sideway falls. The objective of this study is to predict the risk of femoral bone fractures in total hip arthroplasty (THA) and resurfacing hip arthroplasty (RHA). A computed tomography (CT) based on finite element analysis was conducted to demonstrate damage formation in a three dimensional model of HAs. The inhomogeneous model of femoral bone was constructed from a 79 year old female patient with hip osteoarthritis complication. Two different femoral components were modeled with titanium alloy and cobalt chromium and inserted into the femoral bones to present THA and RHA models respectively. The analysis included six configurations, which exhibited various loading and boundary conditions, including axial compression, torsion, lateral bending, stance and two types of falling configurations. The applied hip loadings were normalized to body weight (BW) and accumulated from 1 BW to 3 BW. Predictions of damage formation in the femoral models were discussed as the resulting tensile failure as well as the compressive yielding and failure elements. The results indicate that loading directions can forecast the pattern and location of fractures at varying magnitudes of loading. Lateral bending configuration experienced the highest damage formation in both THA and RHA models. Femoral neck and trochanteric regions were in a common location in the RHA model in most configurations, while the predicted fracture locations in THA differed as per the Vancouver classification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".