The effect of cortex thickness on intact femur biomechanics: A comparison of finite element analysis with synthetic femurs
Bibliographic record
Abstract
Biomechanical studies on femur fracture fixation with orthopaedic implants are numerous in the literature. However, few studies have compared the mechanical stability of these repair constructs in osteoporotic versus normal bone. The present aim was to examine how changes in cortical wall thickness of intact femurs affect biomechanical characteristics. A three-dimensional, linear, isotropic finite element (FE) model of an intact femur was developed in order to predict the effect of bicortical wall thickness, t, relative to the femur's mid-diaphyseal outer diameter, D, over a cortex thickness ratio range of 0 < or = t/D < or = 1. The FE model was subjected to loads to obtain axial, lateral, and torsional stiffness. Ten commercially available synthetic femurs were then used to mimic 'osteoporotic' bone with t/D = 0.33, while ten synthetic left femurs were used to simulate 'normal' bone with t/D = 0.66. Axial, lateral, and torsional stiffness were measured for all femurs. There was excellent agreement between FE analysis and experimental stiffness data for all loading modes with an aggregate average percentage difference of 8 per cent. The FE results for mechanical stiffness versus cortical thickness ratio (0 < or = t/D < or = 1) demonstrated exponential trends with the following stiffness ranges: axial stiffness (0 to 2343 N/mm), lateral stiffness (0 to 62 N/mm), and torsional stiffness (0 to 198 N/mm). This is the first study to characterize mechanical stiffness over a wide range of cortical thickness values. These results may have some clinical implications with respect to appropriately differentiating between older and younger human long bones from a mechanical standpoint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".