Evaluating the Bending Response of Two Osseointegrated Transfemoral Implant Systems Using 3D Digital Image Correlation
Bibliographic record
Abstract
Osseointegrated transfemoral implants have been introduced as a prosthetic solution for above knee amputees. They have shown great promise, providing an alternative for individuals who could not be accommodated by conventional, socket-based prostheses; however, the occurrence of device failures is of concern. In an effort to improve the strength and longevity of the device, a new design has been proposed. This study investigates the mechanical behavior of the new taper-based assembly in comparison to the current hex-based connection for osseointegrated transfemoral implant systems. This was done to better understand the behavior of components under loading, in order to optimize the assembly specifications and improve the useful life of the system. Digital image correlation was used to measure surface strains on two assemblies during static loading in bending. This provided a means to measure deformation over the entire sample and identify critical locations as the assembly was subjected to a series of loading conditions. It provided a means to determine the effects of tightening specifications and connection geometry on the material response and mechanical behavior of the assemblies. Both osseoinegrated assemblies exhibited improved strength and mechanical performance when tightened to a level beyond the current specified tightening torque of 12 N m. This was shown by decreased strain concentration values and improved distribution of tensile strain. Increased tightening torque provides an improved connection between components regardless of design, leading to increased torque retention, decreased peak tensile strain values, and a more gradual, primarily compressive distribution of strains throughout the assembly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".