Prophylactic augmentation implants in the proximal femur for hip fracture prevention: An in silico investigation of simulated sideways fall impacts
Bibliographic record
Abstract
Femoral fractures from sideways falls in the elderly are associated with significant rates of morbidity and mortality. Approaches to prevent these catastrophic injuries include pharmacological treatments, which have limited efficacy. Prophylactic femoral augmentation systems are a promising alternative that are gaining prominence by addressing the most debilitating osteoporosis-related fracture. We have developed finite element models (FEMs) of a novel experimental sideways fall simulator for cadavers. By virtue of the range of specimens and injury outcomes, these FEMs are well-suited to the evaluation of such implants. The purpose of this study was to use the FEMs to evaluate the mechanical effectiveness of three different prophylactic femoral augmentation systems. Models of the Y-Strut® (Hyprevention®, Pessac, France), Gamma Nail® (Stryker, Kalamazoo, USA), and a simple lag screw femoral fracture implant systems were placed into FEMs of five cadaveric pelvis-femur constructs embedded in a soft tissue surrogate, which were then subject to simulated sideways falls at seven impact velocities. Femur-only FEMs were also evaluated. Peak impact forces and peak acetabular forces were examined, and failure was evaluated using a strain-based criterion. We found that the femoral augmentation systems increased the peak forces prior to fracture, but were unable to prevent fracture for severe impacts. The Gamma Nail® system consistently produced the largest strength increases relative to the unaugmented femur for all five specimens in both the pendulum-drop FEMs and the femur-only simulations. In some cases, the same implant appeared to cause fractures in the acetabulum. The femur-only FEMs showed larger force increases than the pendulum-drop simulations, which suggests that the results of the femur-only simulations may not represent sideways falls as accurately as the soft tissue-embedded pendulum-drop simulations. The results from this study demonstrate the ability to simulate a high energy phenomenon and the effect of implants in an in silico environment. The results also suggest that implants could increase the force applied to the proximal femur during impact. Fracture outcomes from the tested implants can be used to inform the design of future devices, which reaffirms the value of modelling with biofidelic considerations in the implant design process. To the authors' knowledge, this is the first paper to use more complex biofidelic FEMs to assess prophylactic femoral augmentation methods.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".