Development of a Full Flexion 3D Musculoskeletal Model of the Knee Considering Intersegmental Contact During High Knee Flexion Movements
Bibliographic record
Abstract
A musculoskeletal model of the right lower limb was developed to estimate 3D tibial contact forces in high knee flexion postures. This model determined the effect of intersegmental contact between thigh-calf and heel-gluteal structures on tibial contact forces. This model includes direct tracking and 3D orientation of intersegmental contact force, femoral translations from in vivo studies, wrapping of knee extensor musculature, and a novel optimization constraint for multielement muscle groups. Model verification consisted of calculating the error between estimated tibial compressive forces and direct measurements from the Grand Knee Challenge during movements to ∼120° of knee flexion as no high knee flexion data are available. Tibial compression estimates strongly fit implant data during walking (R2 = .83) and squatting (R2 = .93) with a root mean squared difference of .47 and .16 body weight, respectively. Incorporating intersegmental contact significantly reduced model estimates of peak tibial anterior-posterior shear and increased peak medial-lateral shear during the static phase of high knee flexion movements by an average of .33 and .07 body weight, respectively. This model supports prior work in that intersegmental contact is a critical parameter when estimating tibial contact forces in high knee flexion movements across a range of culturally and occupationally relevant postures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".