Influence of test paradigm on loading dynamics during proximal femur fracture tests simulating sideways falls
Bibliographic record
Abstract
Fall-related hip fractures are a serious public health issue in older adults. As most mechanistic hip fracture risk prediction models incorporate tissue tolerance, test methods that can accurately characterize the fracture force of the femur (and factors that influence it) are imperative. While bone possesses viscoelastic properties, experimental characterization of rate-dependencies has been inconsistent in the whole-femur literature. The goal of this study was to investigate the influence of experimental paradigm on loading rate and fracture force (both means and variability) during mechanical tests simulating lateral fall loadings on the proximal femur. Six pairs of matched femurs were split randomly between two test paradigms: a ‘lower rate’ materials testing system (MTS) with a constant displacement rate of 60 mm/s, and a hip impact simulator (HIT) comprised of a custom-built vertical drop tower utilizing an impact velocity of 4 m/s. The loading rate was 88-fold higher for the HIT (mean (SD) = 2465.49 (807.38) kN/s) compared to the MTS (27.78 (10.03) kN/s) paradigm. However, no difference in fracture force was observed between test paradigms (mean (SD) = 4096.4 (1272.6) N for HIT, and 3641.3 (1285.8) N for MTS). Within-paradigm variability was not significantly different across paradigms for either loading rate or fracture force (coefficients of variation ranging from 0.311 to 0.361). Within each test paradigm, significant positive relationships were observed between loading rate and fracture force (HIT adjusted R2 = 0.833, p = 0.007; MTS adjusted R2 = 0.983, p < 0.0001). Overall, this study provides evidence that energy-based impact simulators can be a valid method to measure femoral bone strength in the context of fall-related hip fractures. This study motivates future research to characterize potential non-linear relationships between loading rate and fracture threshold at both macro and microscales.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".