Previous Damage Accumulation Can Influence Femoral Fracture Strength: A Finite Element Study
Bibliographic record
Abstract
To manage osteoporotic hip fracture risk, it is necessary to understand failure mechanisms of bone at both the material and organ level. The structural response of bone is dependent on load history. Repeated loading causes progressive microstructural cracking, resulting in reduced apparent-level stiffness and, if damage is significant, reductions to peak load bearing capability. However, the effect of previous damage accumulation has not been well explored at the organ level. It was hypothesized that femoral fracture load and fracture pattern may be sensitive to damage accumulation from previous loading events. Six cadaveric specimens were used to develop patient specific finite element (FE) models from quantitative tomographic (qCT) scans. Material properties were assigned from qCT intensity at each element location, and damage evolution was predicted using a previously validated quasi-brittle FE model. Three scenarios were investigated: stumble followed by another stumble (S-S), fall followed by another fall (F-F), and stumble followed by a fall (S-F). Fracture load and pattern were compared to FE predictions for a single stumble (S) or single fall (F) loading event. Most specimens were resilient to accumulated damage, showing little (<5%) change in fracture load from the multiple-load scenarios (S-S, F-F, and S-F) compared to an equivalent single load scenario (S or F). Only one specimen demonstrated moderate (5-15%) reductions in strength from all three multiple-load scenarios. However, two specimens experienced moderate (20-30%) increase in fracture load in some load cases. In these cases, initial damage caused the load to be more evenly distributed upon subsequent loading events. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2197-2203, 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".