Nondestructive Assessment of Growing Rat Tibial Mechanical Properties Under Three-Point Bending: A Microcomputed Tomography Based Finite Element Study
Bibliographic record
Abstract
Microcomputed tomography (micro-CT) based finite element models (FEM) are efficient tools to assess bone mechanical properties. Although they have been developed for different animal models, there is still a lack of data for growing rat long bone models. This study aimed at developing and calibrating voxel-based FEMs using micro-CT scans and experimental data. Twenty-four tibiae were extracted from rats aged 28, 56, and 84 days old (d.o.) (n = 8/group), and their stiffness values were evaluated using three-point bending tests. Prior to testing, tibiae were scanned, reconstructed, and converted into FEM composed of heterogeneous bone properties based on pixel grayscales. Three element material laws (one per group) were calibrated using back-calculation process based on experimental bending data. Two additional specimens per group were used for model verification. The calibrated rigidity-density (E-ρ) relationships were different for each group: E28 = 10,320·ρash3.45; E56 = 43,620·ρash4.41; E84 = 20,090·ρash2.0. Obtained correlations between experimental and FEM stiffness values were 0.43, 0.10, and 0.66 with root-mean-square error (RMSE) of 14.4%, 17.4%, and 15.2% for 28, 56, and 84 d.o. groups, respectively. Prediction errors were less than 13.5% for 28 and 84 d.o. groups but reached 57.1% for the 56 d.o. group. Relationships between bone physical and mechanical properties were found to change during the growth, similarly to bending stiffness values, which increased with bone development. The reduced correlation observed for the 56 d.o. group may be related to the pubescent transition at that age group. These FE models will be useful for investigation of bone behavior in growing rats.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".