Effects of dynamization timing and degree on bone healing of different fracture types
Bibliographic record
Abstract
Dynamization, that is, increasing interfragmentary movement (IFM) by reducing fixation stiffness from a rigid to a more flexible state, has been successfully used in clinical practice to promote fracture healing. However, it remains unclear how dynamization timing and degree affect bone healing of different fracture types. Finite element models of tibial fractures based on the OTA/AO classification (Simple: A1-Spiral, A2-Oblique, A3-Transverse; Wedge: B2-Spiral, B3-Fragmented; Complex: C2-Segment, C3-Irregular), in combination with fuzzy logic-based mechano-regulatory tissue differentiation algorithms, were used to simulate the healing process when dynamization of varied degrees (dynamization coefficient or DC = 0-0.9; 0.9 represents 90% reduction in the fixation stiffness relative to a rigid fixation) were applied at different time points after fracture. The fuzzy logic-based algorithms have been validated with a preclinical animal model. The results showed that the healing responses of type A fractures were more sensitive to the changes in dynamization degree and timing comparing with type B or C fractures. Additionally, the optimal dynamization regime for each fracture type was different. For type A fractures, a moderate dynamization degree (e.g., DC = 0.5) applied after Week 1 promoted the recovery of biomechanical integrity. For type B and C fractures, the effective dynamization included a greater dynamization degree (DC = 0.7) applied after Week 2. Our results further demonstrated that the fracture morphology affected interfragmentary strain environments within the callus, leading to varied healing results for different fracture types. These results suggest that the effects of dynamization are highly dependent of the fracture types. Therefore, specific dynamization strategies should be chosen for different fracture types to achieve optimal healing outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".