Muscle-Driven Total Knee Replacement Stability with Virtual Ligaments
Bibliographic record
Abstract
Knee joint stability comprises passive (ligaments), active (muscles), and static (articular congruency) contributors. The stability of total knee replacement (TKR) implants can be assessed pre-clinically using joint motion simulators. However, contemporary testing methods with these platforms do not accurately reproduce the biomechanical contributions of passive stabilizers, active stabilizers, or both. A key component of joint stability is therefore missing from laxity tests. A recently developed muscle actuator system (MAS) pairs the quadriceps-driven motion capabilities of an Oxford knee simulator with the prescribed displacements and laxity testing methods of a VIVO robotic knee testing system, which also includes virtual ligament capabilities. Using a TKR-embedded non-cadaveric joint analogue, TKR with two different virtual ligament models were compared to TKR with no active ligaments. Laxity limits were then obtained for both developed models using the conventional style of laxity testing (the VIVO's force/displacement control) and compared with results obtained under similar conditions with the MAS (gravity-dependent muscle control). Differences in joint control methods identified the need for muscle forces providing active joint stability, while differences in the effects of the virtual ligament models identified the importance of physiological representations of collateral ligaments during testing.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".