A Finite Element Study of the Universality and Scalability of an Optimized Universal Talus Implant
Bibliographic record
Abstract
Total talus replacement is an alternative treatment to ankle fusion for talus fractures resulting from avascular necrosis and collapse. It involves the complete replacement of the human talus bone with an artificial implant that allows for maintaining ankle joint functionality. Universal talus implants have been proposed and proved feasible as a replacement for custom-made ones, reducing costs and implant development times. Nevertheless, the universal implants remain heavy given their solid nature and materials used, leading to an unnatural feel and potentially further complications for the patient. Consequently, the implants have been redesigned using topology optimization, resulting in significantly lighter implants with high safety factors when simulated under three common foot postures, namely neutral, dorsi- and plantar-flexion, for a single human subject's ankle joint geometry. Therefore, in order to evaluate the universality and scalability of the optimized universal implant, it was scaled to different sizes, for three different bone geometries, under the aforementioned postures. Its performance in terms of stress distributions in the implant in addition to the contact characteristics with the surrounding bone cartilages was studied using finite element analysis. When scaled to smaller or larger sizes, depending on the subject, the resulting safety factors for subjects 1-3 were 4.65, 3.19, and 4.33, respectively, for maximum von Mises stresses (in MPa) of 236.4, 344.6, and 254.1, respectively, deeming the optimized implant scalable. Similarly, in addition to the obtained stresses, the contact characteristics were in agreement with the expected implant behavior on the surrounding bone cartilages. Thus, the implant was also deemed universal after behaving as intended under different sizes for different bone geometries. Ultimately, while mechanical testing is required to determine clinical feasibility, it is currently not necessitated that the previously-developed universal implant be re-optimized.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".