Landmark-free statistical shape modelling reveals effects of age and sex on whole muscle morphology among the triceps surae
Bibliographic record
Abstract
The shape of skeletal muscle has important influences on muscle function, yet studies of three-dimensional shape variation are rarely performed. Analysis of muscle shape variation using traditional tools is limited by lack of anatomical landmarks, but modern landmark-free methods provide new opportunities to study complex shapes. We used generalized Procrustes surface analysis to characterize shape variation among the triceps surae: medial gastrocnemius (MG), lateral gastrocnemius (LG) and soleus (SOL), digitized using magnetic resonance imaging from 21 younger (8 females, 13 males; 24.6 ± 4.3 years) and 15 older (6 females; 9 males; 70.4 ± 2.4 years) physically active participants. In both gastrocnemii, the first principal component (PC) of shape variance was related to muscle width and thickness. The second PC was related to variation in the MG's insertion and variation in thickness along the LG long axis. In the SOL, the first PC was related to overall muscle thickness and length while the second PC captured variation in lateral margin thickness and curvature of the medial border. Muscle shape differed between young and older adults in MG and LG, while SOL shape differed between males and females. These findings demonstrate statistical shape modelling as a promising tool for disentangling multiple influences on skeletal muscle shape and provide important input for future biomechanical modelling investigations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".