Femoral fracture load and fracture pattern is accurately predicted using a gradient-enhanced quasi-brittle finite element model
Bibliographic record
Abstract
Nonlinear finite element (FE) modeling can be a powerful tool for studying femoral fracture. However, there remains little consensus in the literature regarding the choice of material model and failure criterion. Quasi-brittle models recently have been used with some success, but spurious mesh sensitivity remains a concern. The purpose of this study was to implement and validate a new model using a custom finite element designed to mitigate mesh sensitivity problems. Six specimen-specific FE models of the proximal femur were generated from quantitative tomographic (qCT) scans of cadaveric specimens. Material properties were assigned a-priori based on average qCT intensities at element locations. Specimens were experimentally tested to failure in a stumbling load configuration, and the results were compared to FE model predictions. There was a strong linear relationship between FE predicted and experimentally measured fracture load ( R 2 = 0.79), and error was less than 14% over all cases. In all six specimens, surface damage was observed at sites predicted by the FE model. Comparison of qCT scans before and after experimental failure showed damage to underlying trabecular bone, also consistent with FE predictions. In summary, the model accurately predicted fracture load and pattern, and may be a powerful tool in future studies.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".