Design and virtual evaluation of a customized surface-guided knee implant
Bibliographic record
Abstract
Although total knee arthroplasty is generally a successful operation, many studies have shown that it results in significant alterations in the kinematics of the joint, which cause limitations in performing the activities of daily living. This study aimed to define the design features for a customized surface-guided total knee replacement and to evaluate the kinematic outcomes. Magnetic resonance imaging data of the knee joint are used to generate the design features as they relate to the functionality of the implant. The motion is guided by considering a partial ball and socket configuration on the medial condyle and varying radii of curvature on the lateral articulating surface. A virtual simulation of the behavior of the surface-guided total knee replacement was performed to investigate the motion patterns of this total knee replacement under gait and squatting loading conditions. Results of the virtual simulation show that flexion and extension of the knee make the center of the lateral condyle move more naturally in the posterior and anterior directions, in comparison to the center of the medial condyle. Such guidance is achieved as a result of the novel customized designed contact between the articulating surfaces. The proposed customized surface-guided total knee replacement provides patterns of motion close to the expected more natural target, not only during a gait cycle but also as the knee flexes to higher degrees during squatting. Major design features include location and orientation of the flexion and pivoting axes, the trace of the contact points on the tibia, and the radii of the guiding arcs on the lateral condyle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".