Comparing Patellofemoral Kinematics Assessed With a Novel Muscle Actuator System and an Oxford Rig Using Noncadaveric Knees
Bibliographic record
Abstract
Total knee replacement (TKR) failure, low patient satisfaction and high revision surgery rates may stem from insufficient preclinical testing. Conventional joint motion simulators for preclinical testing of TKR implants manipulate a knee joint in force, displacement, or simulated muscle control. However, a rig capable of using all three control modes has yet to be described in literature. This study aimed to validate a novel platform, the muscle actuator system (MAS), that can generate gravity-dependent, quadriceps-controlled squatting motions representative of an Oxford rig knee simulator and is mounted onto a force/displacement-control-capable joint motion simulator. Synthetic knee joint phantoms were created that comprised revision TKR implants and key extensor and flexor mechanism analogues, but no ligaments. The combined system implemented a constant force vector acting from simulated hip-to-ankle coordinates, effectively replicating gravity as observed in an Oxford rig. Quadriceps forces and patellofemoral joint kinematics were measured to assess the performance of the MAS and these tests showed high levels of repeatability and reproducibility. Forces and kinematics measured at a nominal patellar tendon length, and with patella alta and baja, were compared against those measured under the same conditions using a conventional Oxford rig, the Pennsylvania State Knee Simulator (PSKS). There was disagreement in absolute kinematics and muscle forces, but similar trends resulting from changing prosthesis design or patellar tendon length.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".