The combined effects of dynamization time and degree on bone healing
Bibliographic record
Abstract
Dynamization, increasing the interfragmentary movement (IFM) by reducing the fixation stiffness from a rigid to a more flexible condition, is widely used clinically to promote fracture healing. However, it remains unknown how dynamization degree (relative change in fixation stiffness/IFM from a rigid to a flexible fixation) affects bone healing at various stages. To address this issue, we used a fuzzy logic-based mechano-regulated tissue differentiation algorithm on published experimental data from a sheep osteotomy healing model. We applied a varied degree of dynamization, from 0 (fully rigid fixation) to 0.9 (90% reduction in stiffness relative to the rigid fixation) after 1, 2, 3, and 4 weeks of osteotomy (R1wF, R2wF, R3wF, and R4wF) and computationally evaluated bone regeneration and biomechanical integrity over the healing process of 8 weeks. Compared with the constant rigid fixation, early dynamization (R1wF and R2wF) led to delays in bone bridging and biomechanical recovery of the osteotomized bone. However, the effect of early dynamization on healing was dependent of the degree of dynamization. Specifically, a higher dynamization degree (e.g., 0.9 for R1wF) led to a prolonged delay in bone bridging and largely unrecovered bending stiffness (48% relative to the intact bone), whereas a moderate degree of dynamization (e.g., 0.5 or 0.7) significantly enhanced bone formation and biomechanical properties of the osteotomized bone. These results suggest that dynamization degree and timing interactively affect the healing process. A combination of early dynamization with a moderate degree could enhance the ultimate biomechanical recovery of the fractured bone.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".