The effect of load application rate on the biomechanics of synthetic femurs
Bibliographic record
Abstract
Biomechanical investigations are increasingly using commercially available synthetic femurs as surrogates for human cadaveric femurs. However, the rate of force application in testing these artificial femurs appears to be chosen arbitrarily without much consideration to their visco-elastic time-dependent nature. The aim of this study, therefore, was to examine the effect of loading rate on the mechanical behaviour of synthetic femurs. Ten left, medium, fourth-generation composite femurs (Model 3403, Pacific Research Laboratories, Vashon, WA, USA) were fixed distally into cement-filled steel cubic chambers for mounting into a mechanical tester. In randomized order, each of the ten femurs was loaded at rates of 1, 2.5, 5, 7.5, 10, 20, 30, 40, 50, and 60 mm/min to obtain axial, lateral, and torsional stiffness. Axial stiffness showed an aggregate average value of 1742.7 +/- 174.7 N/mm with a high linear correlation with loading rate (R2 = 0.80). Lateral stiffness yielded an aggregate average value of 56.9 +/- 10.2 N/mm and was linearly correlated with loading rate (R2 = 0.85). Torsional stiffness demonstrated an aggregate average value of 176.9 +/- 14.5 N/mm with a strong linear correlation with loading rate (R2 = 0.59). Despite the high correlations between stiffness and speed, practically this resulted in an overall average difference between the lowest and highest stiffness of only 4 per cent. Moreover, no statistical comparisons between loading rates for axial, lateral, or torsional test modes showed differences (p > or = 0.843). Future biomechanical investigators utilizing these synthetic femurs need not be concerned with loading rate effects over the range tested presently. This is the first study in the literature to perform such an assessment.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".