Posterior-stabilized total knee arthroplasty kinematics and joint laxity: A hybrid biomechanical study
Bibliographic record
Abstract
BACKGROUND: Posterior-stabilized (PS)-total knee arthroplasty (TKA) arose as an alternative to cruciate-retaining (CR)-TKA in the 1970s. Since then, it has become a popularly utilized TKA design with outcomes comparable to CR-TKA. The post-cam mechanism is unique to PS-TKA as it substitutes the function of the posterior cruciate ligament (PCL). The study aimed to understand the kinematic and laxity changes in PS-TKA with under- and overstuffing of the tibiofemoral joint space with the polyethylene (PE) insert. METHODS: This study employed a hybrid computational-experimental joint motion simulation on a VIVO 6 degrees of freedom (6-DoF) joint motion simulator (AMTI, Watertown, MA, USA). Physical prototypes of a virtually-performed TKA in mechanical alignment (MA) and kinematic alignment (KA) based on cadaveric CT scans and a virtual ligament model were utilized. The reference, understuffed (down 2 mm) and overstuffed (up 2 mm) joint spaces were simulated, neutral flexion and laxity testing loads and motions were performed for each configuration. RESULTS: The PE insert thickness influenced post-cam engagement, which occurred after 60º in the overstuffed configurations, after 60º-75º in the reference configurations and after 75º in the understuffed configurations. The understuffed configurations, compared to the reference configurations, resulted in a mean 2.0º (28%) and 2.0º (31%) increase in the coronal laxity in MA and KA respectively. The overstuffed configurations, compared to the reference configuration, resulted in an increase in the mean joint compressive forces (JCFs) by 73 N (61%) and 77 N (62%) in MA and KA models, respectively. CONCLUSIONS: The under- and overstuffing in PS-TKA alter the kinematics with variable effects. Understuffing decreases the stability, JCFs and inverse with overstuffing. Subtle changes in the PE insert thickness alter the post-cam mechanics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".