Biophysical regulation of osteotomy healing: An animal study
Bibliographic record
Abstract
BACKGROUND: Osteotomies have been performed for centuries yet there remains a remarkable lack of consensus on optimal methods for cutting bone. There is universal agreement, however, that preserving cell viability is critical. PURPOSE: To identify mechanobiological parameters influencing bone formation after osteotomy site preparation. MATERIALS AND METHODS: A murine maxillary osteotomy model was used to evaluate healing. Computational modeling characterized stress and strain distributions in the osteotomy, as well as the magnitude and distribution of heat generated by drilling. The impact of osteocyte death and bone composition were assessed using molecular and cellular assays. RESULTS: The phases of osteotomy healing in mice align closely with results in large animals; in addition, molecular analyses extended our understanding of osteoprogenitor cell proliferation, differentiation, and mineralization. Computational analyses provided insights into temperature changes caused by drilling and the mechanobiological state in the healing osteotomies, while concomitant cellular assays correlate drill speed with osteocyte apoptosis and bone resorption. Even when drilling was controlled, trabeculated, spongy (Type III) bone healed faster than densely lamellar (Type I) bone because of the abundance of Wnt responsive osteoprogenitor cells in the former. CONCLUSIONS: These data provide a mechanobiological framework for evaluating tools and technologies designed to improve osteotomy site preparation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".