Computation of bone remodelling after Duracon knee arthroplasty using a thermodynamic-based model
Bibliographic record
Abstract
The present study utilizes a recently developed literature model for the bone remodelling process to predict the evolution of bone density following Duracon total knee arthroplasty (TKA). In this model, which is based on chemical kinetics and irreversible thermodynamics, bone is treated as a self-organizing system capable of exchanging matter, energy, and entropy with its surroundings. Unlike previous models in which mechanical loading is regarded as the only stimulus for bone remodelling, the present model establishes a unique coupling between mechanical loading and the chemical reactions involved in the process of bone remodelling. This model was incorporated into the finite element software ANSYS by means of a macro to compute density distribution in distal femoral bone both before and after TKA. Consistent with dual-energy X-ray absorptiometry (DEXA) scans reported in the literature, the results showed that the most severe bone loss occurs in the anterior region of the distal femur and that there is more bone resorption in the lateral than the medial condyle following TKA. Furthermore, the bone density distribution predicted using the present model showed a gradual and uniform pattern and thus a more realistic bone evolution contrary to the strain energy density model, where there is no gradual bone density evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".