Optimization of the density–elasticity relationship for rabbit hindlimb bones
Bibliographic record
Abstract
The rabbit is a popular experimental model in orthopaedic biomechanics due to the presence of natural Haversian remodeling, allowing for better translational relevance to the mechanobiology of human bone over traditional rodent models. Although rabbits are often used with computational modeling approaches such as the finite element (FE) method, a validated and widely agreed upon density–elasticity relationship, which is required to make subject-specific predictions, does not exist. Therefore, the purpose of this study was to determine and validate an accurate density–elasticity relationship for rabbit hindlimb bones using mathematical optimization. Fourteen tibiae and thirteen femora were harvested from New Zealand White Rabbits, imaged with computed tomography (CT), and cyclically loaded in uniaxial compression while strain gauge rosette data were recorded. The CT images were processed into subject-specific FE models which were used in a Nelder–Mead optimization routine to determine a density–elasticity relationship that minimized the error between experimentally measured and FE-predicted principal strains. Optimizations were performed for the tibiae and femora independently, and for both bones combined. A subset of 4 tibiae and 4 femora that were excluded from the optimization were then used to validate the derived relationships. All equations that were determined by the initial optimization exhibited a Y = X type of relationship with strong correlations (Tibiae: R 2 = 0 . 96 ; Femora: R 2 = 0 . 85 ; Combined: R 2 = 0 . 90 ) and good agreement. The validation groups yielded similar results with strong correlations (Tibiae: R 2 = 0 . 94 ; Femora: R 2 = 0 . 87 ; Combined: R 2 = 0 . 91 ). These findings suggest that any of the derived density–elasticity relationships are suitable for computational modeling of the rabbit hindlimb and that a single relationship could be used for the whole rabbit hindlimb in studies where greater computational efficiency is necessary. • We have validated density–elasticity relationships for the rabbit hindlimb bones. • Strongly correlated ( R 2 ≥ 0 . 85 ) strain predictions were observed. • All relationships provided accurate strain predictions with Y = X type of behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".