Individual Differences in Women During Walking Affect Tibial Response to Load Carriage: The Importance of Individualized Musculoskeletal Finite-Element Models
Bibliographic record
Abstract
Subject-specific features can contribute to the susceptibility of an individual to stress fracture. Here, we incorporated tibial morphology and material properties into a standard musculoskeletal finite-element (M/FE) model and investigated how load carriage influences joint kinetics and tibial mechanics in women. We obtained the morphology and material properties of the tibia from computed tomography images for women of three distinctly different heights, 1.51 m (short), 1.63 m (medium), and 1.75 m (tall), and developed individualized M/FE models for each. Then, we calculated joint and muscle forces, and subsequently, tibial stress/strain for each woman walking at 1.3 m/s under various load conditions (0, 11.3, or 22.7 kg). Among the subjects investigated, using individualized and standard M/FE models, the joint reaction forces (JRFs) differed by up to 4 (hip), 22 (knee), and 26% (ankle), and the 90th percentile von Mises stress by up to 30% (tall woman). Load carriage evoked distinct biomechanical responses, with a 22.7-kg load decreasing the peak hip JRF during late stance by ∼18% in the short woman, while increasing it by ∼39% in the other two women. It also increased peak knee and ankle JRFs by up to ∼48 (tall woman) and ∼36% (short woman). The same load increased the 90th percentile von Mises stress (and corresponding cumulative stress) by 31 (28), 22 (30), and 27% (32%) in the short, medium, and tall woman, respectively. Our findings highlight the critical role of individualized M/FE models to assess mechanical loading in different individuals performing the same physical activity.
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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 | 0.001 |
| 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) | 0.001 | 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".