Structure Modeling and Mechanical Analyses of Meniscal Implants Based on Triply Periodic Minimal Surfaces
Bibliographic record
Abstract
As an important part of the knee joint, the meniscus plays a role in transmitting load, absorbing oscillation, and improving joint stability [1].However, joint disease, degeneration, trauma and other causes may cause damage to the meniscus.Meniscus transplantation can solve diseases such as osteoarthritis caused by meniscus loss, but there are some problems such as limited stent replacement, immune response, and structural mismatch [2, 3].There is currently a problem of mismatch in mechanical properties between commercial meniscus implants and natural meniscus, which is not conducive to long-term implantation [4].Therefore, a porous polycarbonate-polyurethane meniscus implant based on a very small threeperiod surface is proposed.First, a 3D model of the knee joint was established based on the CT scan results of the knee joint [5].The porous element is constructed by Primitive minimal surface.After Boolean operation with the outer meniscus structure, a series of implant structures with different pore sizes or porosity are obtained by adjusting the surface construction parameters.Then, finite element simulation was performed to compare the mechanical changes of articular cartilage and bilateral meniscus in the knee joint of the natural meniscus and the designed porous meniscus.The results show that the use of a porous meniscal implant can effectively reduce the compressive stress and shear stress concentration on the femoral cartilage and the tibial cartilage.At the same time, changes in the structural parameters of the porous implant affect the stress of the articular cartilage.In addition to having good mechanical properties, the structure can also be rapidly formed by three-dimensional printing technology, which provides a new idea for clinical application.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".