A multiphysics stimulus for continuum mechanics bone remodeling
Bibliographic record
Abstract
Bone remodelling is a complex phenomenon during which old and damage bone is removed and replaced with new one to ensure the physiological functions of the skeletal system.It involves many biological, mechanical, chemical processes at different scales.The objective of the present work is to predict the kinetics of bone density evolution by taking into account both the mechanical and the biological frameworks.In order to do so, we propose a new computational model in which the global stimulus triggering bone remodelling is the result of the contribution of a mechanical (i.e.external loads and consequent strain energy), a cellular (i.e.osteoblasts and osteoclasts activities) and a molecular (i.e.oxygen and glucose supply) stimulus.The evolution of the bone density depends on the overall behaviour of the global stimulus.More specifically, when the global stimulus is positive, bone synthesis occurs, whereas when the global stimulus is negative, resorption takes place.Although the theoretical model has been applied on a very simple two-dimensional geometry, the final results provide new insights on the influence of each stimulus on the bone remodelling process.In particular, we confirm that mechanics plays a critical role and affects the kinetics of bone reconstruction, but it highly depends on the biological events and the distribution of bone density.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".