Experimental validation of finite element predicted bone strain in the human metatarsal
Bibliographic record
Abstract
Metatarsal stress fracture is a common injury observed in athletes and military personnel. Mechanical fatigue is believed to play an important role in the etiology of stress fracture, which is highly dependent on the resulting bone strain from the applied load. The purpose of this study was to validate a subject-specific finite element (FE) modeling routine for bone strain prediction in the human metatarsal. Strain gauge measurements were performed on 33 metatarsals from seven human cadaveric feet subject to cantilever bending, and subject-specific FE models were generated from computed tomography images. Material properties for the FE models were assigned using a published density-modulus relationship as well as density-modulus relationships developed from optimization techniques. The optimized relationships were developed with a ‘training set’ of metatarsals (n = 17) and cross-validated with a ‘test set’ (n = 16). The published and optimized density elasticity equations provided FE-predicted strains that were highly correlated with experimental measurements for both the training (r 2 ≥ 0.95) and test (r 2 ≥ 0.94) sets; however, the optimized equations reduced the maximum error by 10% to 20% relative to the published equation, and resulted in an X = Y type of relationship between experimental measurements and FE predictions. Using a separate optimized density-modulus equation for trabecular and cortical bone did not improve strain predictions when compared to a single equation that spanned the entire bone density range. We believe that the FE models with optimized material property assignment have a level of accuracy necessary to investigate potential interventions to minimize metatarsal strain in an effort to prevent the occurrence of stress fracture.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".