Computational Simulation of Mechanical Microenvironment of Early Stage of Bone Healing under Locking Compression Plate with Dynamic Locking Screws
Bibliographic record
Abstract
It is well known that bone healing outcomes highly depend on the mechanical microenvironment of the fracture site, and a certain degree of interfragmentary movement (IFM) is essential for indirect (i.e. natural) bone healing. The application of locking compression plate (LCP) internal fixation in the treatment of bone fracture is a common practice which leads to early mobility and full function of the fractured extremity. However should the fixation configuration be too stiff, it might result in delayed healing or asymmetric tissue development across the fracture site due to the fact that IFM in near cortex area is too small to promote healing. Dynamic locking screw (DLS) has been recently designed to tackle this problem by reducing the stiffness of LCP fixation. However, the actual mechano-regulation mechanisms in which DLS uses to regulate the healing process are still not fully understood. The objective of this paper is to develop a computational model to understand the change of mechanical microenvironment of fracture site under LCP with dynamic locking screw in comparison to standard locking screw, and how this change could potentially regulate tissue development within the fracture callus during the healing process.Keywords: bone healing, locking compression plate, dynamic locking screw, finite element modelling
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".