Quantifying the regional variations in the mechanical properties of cancellous bone of the tibia using indentation testing and quantitative computed tomographic imaging
Bibliographic record
Abstract
Finite element models apply material properties using experimentally derived density-modulus equations and computed tomographic image data, yet numerous different equations exist in the literature. The purpose of this study was to experimentally evaluate the distribution of mechanical properties through the proximal tibia and compare with those predicted using existing density-modulus equations. Indentation testing was performed on five cadaveric tibiae, with four slices removed from the proximal epiphysis and metaphysis of each. Elastic modulus and yield strength were identified for each test and grouped into nine transverse regions. These regions were identified on computed tomographic scans, and four density-modulus equations from the literature applied. Errors between measured and predicted modulus were then calculated. Elastic modulus and yield strength varied regionally, with the bone located closest to the joint and in the condyles being strongest and the intercondylar region the weakest. The optimal relationship for predicting modulus varied depending on anatomical region, but generally was best predicted by the Goulet equation. The regions of high strength identified in this study (condyles and proximal regions) can serve as improved sites of attachment for orthopedic devices and should be preserved during surgery, if possible. The substantial regional variations observed herein (almost a threefold change in modulus across different regions) should be incorporated into finite element models and applied using the Goulet density-modulus equation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".