The Impact of Osteoarthritis‐Specific Anatomical Features and Gait Patterns on Medial Compartment Loading in the Presence of Ligament Laxity
Bibliographic record
Abstract
Structural changes in ligaments, particularly reduced stiffness, contribute to increased knee joint laxity in osteoarthritis (OA) patients. In silico modeling offers a valuable method to systematically assess how OA-related ligament alterations affect knee kinematics and contact mechanics. Understanding these effects requires considering OA-specific variations in joint geometry, alignment, and gait patterns. A previously developed musculoskeletal modeling workflow was used to incorporate common KOA-related anatomical variations and gait pattern variations. A probabilistic simulation approach assessed the impact of ligament-induced joint laxity on medial compartment loading. Ligament stiffness and reference strains were modeled as independent Gaussian distributions, centered at nominal model stiffness (-20%) and slack length (+20%), with standard deviations set at 5% based on literature-reported values. Increased medial compartment loading at the second peak occurred when posterior tibial translation and external tibial rotation were combined with either: (1) a gait pattern involving decreased ankle dorsiflexion and hip external rotation, increased foot eversion, and knee extension, or (2) a gait pattern with increased lumbar extension, trunk ipsilateral side bending, hip internal rotation, and knee internal rotation. These conditions also resulted in the largest shift in the center of pressure. While both anatomical variations and gait patterns influence knee joint loading, ligament stability plays a key role in determining medial compartment loading magnitude and location. These findings highlight the need to monitor ligament constraints in rehabilitation and computational models to develop personalized interventions that minimize excessive joint stress and slow disease progression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".