Development of an Inertia-Driven Model of Sideways Fall for Detailed Study of Femur Fracture Mechanics
Bibliographic record
Abstract
A new method for laboratory testing of human proximal femora in conditions simulating a sideways fall was developed. Additionally, in order to analyze the strain state in future cadaveric tests, digital image correlation (DIC) was validated as a tool for strain field measurement on the bone of the femoral neck. A fall simulator which included models for the body mass, combined lateral femur and pelvis mass, pelvis stiffness, and trochanteric soft tissue was designed. The characteristics of each element were derived and developed based on human data from the literature. The simulator was verified by loading a state-of-the-art surrogate femur and comparing the resulting force-time trace to published, human volunteer experiments. To validate the DIC, 20 human proximal femora were prepared with a strain rosette and speckle paint pattern, and loaded to 50% of their predicted failure load at a low compression rate. Strain rosettes were taken as the gold standard, and minimum principal strains from the DIC and the rosettes were compared using descriptive statistics. The initial slope of the force-time curve obtained in the fall simulator matched published human volunteer data, with local peaks superimposed in the model due to internal vibrations of the spring used to model the pelvis stiffness. Global force magnitude and temporal characteristics were within 2% of published volunteer experiments. The DIC minimum principal strains were found to be accurate to 127±239 μɛ. These tools will allow more biofidelic laboratory simulation of falls to the side, and more detailed analysis of proximal femur failure mechanisms using human cadaver specimens.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".