Patella Apex Influences Patellar Ligament Forces and Ratio
Bibliographic record
Abstract
The relationship between three-dimensional shape and patellofemoral mechanics is complicated. The Wiberg patella classification is a method of distinguishing shape differences in the axial plane of the patella that can be used to connect shape differences to observed mechanics. This study uses the Wiberg patella classification to differentiate variance in a statistical shape model describing changes in patella morphology and height. We investigate how patella morphology influences force distribution within the patellofemoral joint. The Wiberg type I patella has a more symmetrical medial and lateral facet while the type III patella has a larger lateral facet compared to medial. The second principal component of the statistical shape model described shape variation that qualitatively resembled the different Wiberg patellas. We generated patellofemoral morphologies from the statistical shape model and integrated them into a musculoskeletal model with a twelve degrees-of-freedom knee. We simulated an overground walking trial with these morphologies and recorded patellofemoral mechanics and ligament forces. An increase in patellar ligament force corresponded with an increase in patella height. Wiberg type III patellas had a sharper patella apex which related to lower ratios of quadriceps tendon forces to patellar ligament forces. The change in pivot point of the patella affects the ratio of forces as well as the patellofemoral reaction force. This study provides a better understanding of how patella morphology affects fundamental patella mechanics which may help identify at-risk populations for pathology development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".