Knee extension moment arm variations relate to mechanical function in walking and running
Bibliographic record
Abstract
The patellofemoral joint plays a crucial mechanical role during walking and running. It increases the knee extensor mechanism’s moment arm and reduces required knee extension muscle forces to generate the extension moment that supports body weight, prevents knee buckling and propels the centre-of-mass. However, the mechanical implications of moment arm variation caused by patellofemoral and tibiofemoral motion remain unclear. We used a data-driven musculoskeletal model with a 12-degree-of freedom knee to simulate the knee extension moment arm during walking and running. Using a geometric method to calculate the moment arm, we found smaller moment arms during running than walking during the swing phase. Overall, knee flexion causes differences between running and walking moment arms as increased flexion causes a posterior shift in the tibiofemoral rotation axis and patella articulation with the distal femur. Moment arms were also affected by knee motion direction and best predicted by separating by direction instead of across the entire gait cycle. Furthermore, we found high inter-subject variation in the moment arm that was largely explained by out-of-plane motion. Our results are consistent with the concept that shorter moment arms increase the effective mechanical advantage of the knee and may contribute to increased running velocity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".