The Comparison of Density-Elastic Modulus Equations for the Distal Ulna at Multiple Forearm Positions: A Finite Element Study
Bibliographic record
Abstract
The accuracy of an empirically derived density-modulus equation for bone depends upon the loading conditions and anatomic site of bone specimens used for experimentation. A recent study used FE modeling to compare the ability of three density-modulus relationships to predict strain during bending in neutral forearm rotation in the distal ulna; however, due to the inhomogeneous nature of these FE models, the performance of each equation is not necessarily consistent throughout forearm rotation. This issue is addressed in the present study, which compares the performance of these equations in pronation and supination. Strain gauge data were collected at six discreet locations of six ulna specimens loaded in bending at 40° of pronation and supination. Three FE models of each specimen were made, one for each density-modulus relation, and the strain output compared to the experimental data. The equation previously shown to be most accurate in predicting ulnar strain in neutral forearm rotation was also most accurate in pronation and supination. These results identify this one equation as the most appropriate for future FE analysis of the ulna (including adaptive remodeling, and further show that isotropic and inhomogeneous FE bone models may provide consistent results in different planes of bending.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".