Stressed volume estimated by finite element analysis predicts the fatigue life of human cortical bone: The role of vascular canals as stress concentrators
Bibliographic record
Abstract
The fatigue life of cortical bone can vary several orders of magnitude, even in identical loading conditions. A portion of this variability is likely related to intracortical microarchitecture and the role of vascular canals as stress concentrators. The size, spatial distribution, and density of canals determine the peak magnitude and volume of stress concentrations. This study utilized a combination of experimental fatigue testing and image-based finite element (FE) analysis to establish the relationship between the stressed volume (i.e., volume of bone above yield stress) associated with vascular canals and the fatigue life of cortical bone. Thirty-six cortical bone samples were prepared from human femora and tibiae from five donors. Samples were allocated to four loading groups, corresponding to stress ranges of 60, 70, 80, and 90 MPa, then cyclically loaded in zero-compression until fracture. Porosity, canal diameter, canal separation, and canal number for each sample was quantified using X-ray microscopy (XRM) after testing. FE models were created from XRM images and used to calculate the stressed volume. Stressed volume was a good predictor of fatigue life, accounting for 67% of the scatter in fatigue-life measurements. An increase in stressed volume was most strongly associated with higher levels of intracortical porosity and larger canal diameters. The findings from this study suggest that a large portion of the fatigue-life variance of cortical bone in zero-compression is driven by intracortical microarchitecture, and that fatigue failure may be predicted by quantifying the stress concentrations associated with vascular canals.
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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 | 0.002 |
| 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 | 0.000 |
| 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".