The influence of geometric design variables on the kinematic performance of a surface-guided total knee replacement
Bibliographic record
Abstract
OBJECTIVE: Tibiofemoral geometries in a total knee replacement (TKR) affect the performance of an implant during activities of daily living. The specially shaped components of a surface-guided TKR aim to control the tibiofemoral motion, such that a normal pattern of motion is achieved, even at high flexion angles. The purpose of this study was to assess the influence of the design parameters on the kinematic behavior of such an implant. A combination of design variables was determined that resulted in the least deviation from the design kinematic target. METHODS: Six major design variables were considered to generate customized surface-guided TKR candidates. The contribution of these variables was evaluated by principal component analysis considering the input design variables and the results of the kinematic performance from a virtual simulation of deep squatting. The tibial internal-external rotation and the anterior-posterior translation of the medial and lateral femoral condyles were recorded for each design candidate. A quantified objective function of the kinematic behavior was used to define the design with a maximum agreement with the target pattern of motion. RESULTS: The location and orientation of the flexion-extension axis and the tibial slope were the most contributing parameters on the modes of variation. On the other hand, the conformity between the lateral guiding arcs had the least contribution. CONCLUSION: Virtual simulation showed that the current TKR reached deep flexion angles under squat load, while the tibia pivoted around the medial center. The tibial rotation was within the expected range of the IE rotation from healthy joints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".