Multi-objective optimization of an origami inspired super-expandable scaffold for distraction osteogenesis
Bibliographic record
Abstract
The extended bone consolidation period in distraction osteogenesis (DO) is a known clinical challenge. Tissue-engineered scaffolds (TIS) have shown promise in enhancing bone regeneration, but conventional designs are not suitable for the large deformations required in DO. This study presents an origami-inspired super-expandable scaffold (OISES) tailored for DO. A Bayesian machine learning platform was used to predict key structural and mechanical properties influencing bone regeneration, followed by multi-objective optimization. Selected designs were fabricated and tested both virtually and experimentally. The optimized OISES configurations maintain porosity near 80 % and high surface area-to-volume ratios—parameters associated with osteogenesis—while exhibiting large recoverable deformations. Simulations revealed consistent mechanical behavior and strain localization, along with a deformation-dependent increase in permeability and decrease in wall shear stress during scaffold expansion. This transition supports a shift from early mechanical stimulation to enhanced fluid and nutrient transport, mirroring the evolving biological needs during early-stage bone healing. These findings suggest that OISES scaffolds can dynamically adapt to the mechanical and biological demands of DO, offering a new class of tunable scaffolds optimized for patient-specific treatments.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".