Effect of load orientation on finite element strain predictions in a rabbit tibial loading model
Bibliographic record
Abstract
Mechanical loading plays an important role in the maintenance of bone quantity and quality. Rodents are the most frequently used in vivo loading model for examining the relationship between applied mechanical loads and the bone adaptation response, but they do not naturally exhibit human-like intracortical remodeling. Instead, our group has developed a non-invasive in vivo rabbit tibial loading model. This study aimed to develop and validate statically equivalent computed tomography (CT)-based finite element (FE) models of the rabbit tibia to capture the micro-mechanical environment produced by our in vivo mechanical loading device. We further sought to investigate the strain prediction sensitivity to changes in the assumed force vector orientation. Twenty hindlimbs from New Zealand White Rabbits were cyclically loaded in uniaxial compression with strain gauge rosettes affixed to the tibia. The hindlimbs were then disarticulated at the hip, imaged with CT in replica experimental fixtures, and processed into specimen-specific FE models. A mathematical optimization routine was used to determine the individual force vector orientations that minimized the error between FE predicted and experimentally measured bone strains, which yielded highly accurate strain predictions ( R 2 = 0 . 96 ) that exhibited a Y= X type of relationship after bias adjustment. This approach resulted in substantially lower strain prediction errors when compared to models using various single assumed orientation techniques. We also found that even slight deviations in the assumed hindlimb orientation substantially affect strain predictions. These findings suggest that experimentally informed approaches may be useful for hindlimb-specific loading orientations. This work serves to enable future studies examining the mechanobiology of bone adaptation using the rabbit animal model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".