An Investigation on the Influence of Stumbling Loads on Femoral Fracture Risk, Using a Novel Gradient Enhanced Quasi-Brittle Finite Element Model
Bibliographic record
Abstract
Stumbling is associated with large hip contact forces, but it remains unclear whether these events contribute to osteoporotic hip fracture risk.We hypothesized that stumbling may increase risk, either causing fracture directly, or damaging the femur leaving it susceptible to future loading.This hypothesis can be tested in-silico, but previously published finite element (FE) models are susceptible to large errors in predicted fracture load and pattern.We developed and validated a novel gradient enhanced quasi-brittle damage model, for improved fracture prediction, and used the model to assess the influence of stumbling on fracture risk.Preliminary FE models were used to explore relevant physics and boundary conditions (BC's) needed to accurately model the femur in fall and stumbling configurations.The study investigated the influence of different BCs, viscoelasticity, inertial dynamics, and biphasic (pore fluid) effects; the potential importance of these phenomenon had been discussed, but not well-explored, in literature.After implementation, the gradient enhanced quasi-brittle damage model was validated through experimental testing.Average fracture load prediction error was 9.6%, compared to 10%-20% errors reported in previous models and fracture pattern was correctly predicted for all cases, compared to the 60-80% accuracy of previous models.This validated model predicted that four of six specimens had a moderate risk of fracture due to stumbling alone, and risk increased significantly with simulated advanced osteoporosis.The model also predicted compaction of the subcapital region, a pattern consistent with impacted fractures observed in clinical settings.Finally, we investigated if progressive damage accumulation from combinations of stumble and fall could increase fracture risk.Most specimens were resilient to accumulated damage and only one experienced reductions in strength (5-15%) from repeat loading.However, two specimens II experienced moderate (20-30%) increase in fracture load, for some load cases; this was a novel finding.In these cases, initial damage accumulation caused the load to be more evenly distributed upon subsequent loading events.These results suggest that stumbling alone can result in hip fracture and therefore future preventative intervention strategies may be more effective if they target both fall and stumbling induced fractures.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".