Three-Dimensional Musculoaponeurotic Architecture of the Human Lower Limb and its Functional Implications
Bibliographic record
Abstract
The musculoaponeurotic architecture of the human lower limb has not been previously investigated in three-dimension (3D) at the fiber bundle (FB) level. As musculoaponeurotic architecture is a primary determinant of muscle function, the lack of architectural data has limited our understanding of functional characteristics of lower limb musculature and the development of high-fidelity simulation and biomechanical models. The overall objective of this thesis is to capture the 3D musculoaponeurotic architecture of all of the lower limb muscles, and to use this data to elucidate functional capabilities of muscle groups, individual muscles, and/or intramuscular partitions. In the first study, 48,000 FBs and all of the tendinous/aponeurotic components of 59 lower limb muscles were digitized and modelled, as in situ, to construct an architecturally comprehensive 3D lower limb muscle model. A detailed architectural analysis of three lower limb muscles demonstrated that each had unique spatial arrangement and morphology of the musculoaponeurotic elements resulting in functionally relevant intramuscular partitioning. The second study captured the 3D musculoaponeurotic architecture of vastus medialis obliquus (VMO) and longus (VML) in 12 specimens. The comparison of architectural parameters of VMO and VML indicated that the two partitions have distinct functional characteristics. The more vertical line of action (LoA), greater excursion and force-generating capability of VML suggested that it contributes primarily to knee extension, whereas, VMO’s more horizontal LoA, suggested a role in medial patellar stabilization. The third study investigated and quantified the 3D musculoaponeurotic architecture of the great toe muscles, in 10 specimens, at the FB/aponeurosis level. Functional differences were elucidated through the comparison of the architectural parameters of the medial and lateral great toe muscles and their intramuscular partitions. The medial musculature was found to have similar force generating capability as the lateral musculature, suggesting that medial and lateral forces are balanced. In conclusion, the 3 studies captured high-resolution 3D architectural data, which enabled detailed morphologic and architectural analysis of the lower limb musculature not possible to date. This volumetric data can be used for the development of finite element models capable of higher fidelity lower limb muscle simulation than presently available.
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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 | 0.000 |
| 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".