Subject-Specific Modeling of the Proximal Femur During Falls: Dual Importance of Impact Dynamics and Bone Morphology
Bibliographic record
Abstract
Clinical risk factors for hip fracture can influence both fall-induced loading and underlying femur morphology/strength; however, these effects are generally studied in isolation. We evaluated the potential effects of fall-type, biological sex, and trochanteric soft tissue thickness (TSTT) on femoral neck stresses and fracture risk index during lateral impacts. Thirty-two young adults completed voluntary falls, representative of falls in older adults. Peak impact force magnitude, direction, and point of application were extracted and applied to subject-specific beam models generated from dual-energy X-ray absorptiometry (DXA) scans. Falls with loading vectors directed more perpendicular to the femoral shaft were associated with increased compressive stress in the superior-lateral cortex (a demonstrated site of fracture initiation). Despite 44.5% greater impact force among males, no sex-based differences in femoral neck stresses were observed. Low-TSTT participants experienced greater femoral neck stresses than high-TSTT participants despite no differences in impact force magnitude. These findings highlight the importance of considering underlying differences in narrow neck mechanical properties (which vary across sex and TSTT-groups) when assessing tissue-level loading. Consistent with clinical findings, increased TSTT was associated with reduced fracture risk index among females but not males. This study provides novel insights into the mechanistic pathways through which different fall-types, biological sex, and TSTT may modulate hip fracture risk. Coupling of experimental fall simulations with tissue-level models enabled a computationally efficient method to investigate hip fracture risk, which is sensitive to biological variability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".