Virtual Joint Motion Simulator Accurately Predicts Effects of Femoral Component Malalignment during TKA
Bibliographic record
Abstract
Component alignment accuracy during total knee arthroplasty (TKA) has been improving through the adoption of image-based navigation and robotic surgical systems. The biomechanical implications of resulting component alignment error, however, should be better characterized to better understand how sensitive surgical outcomes are to alignment error. Thus, means for analyzing the relationships between alignment, joint kinematics, and ligament mechanics for candidate prosthesis component design are necessary. We used a digital twin of a commercially available joint motion simulator to evaluate the effects of femoral component rotational alignment. As anticipated, the model showed that an externally rotated femoral component results in a knee which is more varus in flexion, with lower medial collateral ligament tension compared to a TKA knee with a neutrally aligned femoral implant. With the simulation yielding logical results for this relatively simple test scenario, we can have more confidence in the accuracy of its predictions for more complicated scenarios.
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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 | 0.003 |
| 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 | 0.000 |
| 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".