Mechanical Fatigue of Bovine Cortical Bone Using Ground Reaction Force Waveforms in Running
Bibliographic record
Abstract
Stress fractures are a common overuse injury among runners associated with the mechanical fatigue of bone. Several in vivo biomechanical studies have investigated specific characteristics of the vertical ground reaction force (vGRF) in heel-toe running and have observed an association between increased loading rate during impact and individuals with a history of stress fracture. The purpose of this study was to examine the fatigue behavior of cortical bone using vGRF-like loading profiles, including those that had been decomposed into their respective impact and active phase components. Thirty-eight cylindrical cortical bone samples were extracted from bovine tibiae and femora. Hydrated samples were fatigue tested at room temperature in zero compression under load control using either a raw (n = 10), active (n = 10), low impact (n = 10), or high impact (n = 8) vGRF profile. The number of cycles to failure was quantified and the test was terminated if the sample survived 105 cycles. Fatigue life was significantly greater for both impact groups compared to the active (p < 0.001) and raw (p < 0.001) groups, with all low impact samples and 6 of 8 high impact samples surviving 105 cycles. The mean (± SD) number of cycles to failure for the active and raw groups was 12,133±11,704 and 16,552±29,612, respectively. The results suggest that loading rates associated with the impact phase of a typical vGRF in running have little influence on the mechanical fatigue behavior of bone relative to loading magnitude, warranting further investigation of the mechanism by which increased loading rates are associated with 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".