Development and validation of a distal radius finite element model to simulate impact loading indicative of a forward fall
Bibliographic record
Abstract
The purpose of this work was to develop and validate a finite element model of the distal radius to simulate impact loading. Eight-node hexahedral meshes of the bone and impactor components were created. Three separate impact events were simulated by altering the impact velocity assigned to the model projectile (pre-fracture, crack and fracture). Impact forces and maximum and minimum principal strains were calculated and used in the validation process by comparing with previously collected experimental data. Three measures of mesh quality (Jacobians, aspect ratios and orthogonality) and four validation methods (validation metric, error assessment, fracture comparisons and ensemble averages) assessed the model. The element Jacobians, aspect ratios and orthogonality measures ranged from 0.08 to 12, 1.1 to 26 and -70° to 80°, respectively. The force and strain validation metric ranged from 0.10 to 0.54 and 0.35 to 0.67, respectively. The estimated peak axial force was found to be a maximum of 28.5% greater than the experimental (crack) force, and all forces fell within ±2 standard deviation of the mean experimental fracture forces. The predicted strains were found to differ by a mean of 33% across all impact events, and the model was found to accurately predict the location and severity of bone damage. Overall, the model presented here is a valid representation of the distal radius subjected to impact.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".