Tibial-fibular geometry and density variations associated with elevated bone strain and sex disparities in young active adults
Bibliographic record
Abstract
Tibial stress fracture is a common injury in runners and military personnel. Elevated bone strain is believed to be associated with the development of stress fractures and is influenced by bone geometry and density. The purpose of this study was to characterize tibial-fibular geometry and density variations in young active adults, and to quantify the influence of these variations on finite element-predicted bone strain. A statistical appearance model characterising tibial-fibular geometry and density was developed from computed tomography scans of 48 young physically active adults. The model was perturbed ±1 and 2 standard deviations along each of the first five principal components to create finite element models. Average male and female finite element models, controlled for scale, were also generated. Muscle and joint forces in running, calculated using inverse dynamics-based static optimization, were applied to the finite element models. The resulting 95th percentile pressure-modified von Mises strain (peak strain) and strained volume (volume of elements above 4000 με) were quantified. Geometry and density variations described by principal components resulted in up to 12.0% differences in peak strain and 95.4% differences in strained volume when compared to the average tibia-fibula model. The average female illustrated 5.5% and 41.3% larger peak strain and strained volume, respectively, when compared to the average male, suggesting that sexual dimorphism in bone geometry may indeed contribute to greater stress fracture risk in females. Our findings identified important features in subject-specific geometry and density associated with elevated bone strain that may have implications for stress fracture risk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".