Development of a customized density—modulus relationship for use in subject-specific finite element models of the ulna
Bibliographic record
Abstract
Assigning an appropriate density-modulus relationship is an important factor when applying inhomogeneous material properties to finite element models of bone. The purpose of this study was to develop a customized density-modulus equation for the distal ulna, using beam theory combined with experimental results. Five custom equations of the form E= ap(b) were used to apply material properties to models of eight ulnae. All equations passed through a point (1.85, Ec), where p = 1.85 g/cm3 represents the average density of cortical bone. For custom equations (1) to (3), Ec was predicted using beam theory, and the value of b was varied within the range reported in the literature. Custom equations (4) and (5) used other values of Ec from the literature, while keeping b constant. Results obtained from the custom equations were compared with those from other equations in the literature, and with experimental results. The beam theory analysis predicted Ec = 21 +/- 1.6 GPa, and the three custom equations using this value tended to have the lowest errors. The power of the equations did not affect the results as much as the value used for Ec. Overall, a customized density-modulus relationship for the ulna was generated, which provided improved results over using previously reported density-modulus equations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".