Reconstruction of Critical‐Sized Bone Defect in Dog Skull by Octacalcium Phosphate Combined with Collagen
Bibliographic record
Abstract
PURPOSE: The present study was designed to investigate whether synthetic octacalcium phosphate (OCP) combined with collagen (OCP/collagen) can repair a critical-sized defect in dog skull. OCP/collagen has been shown to biodegrade and to tend to be replaced by newly formed bone if implanted in rat calvaria defects. MATERIALS AND METHODS: An OCP/collagen disk was prepared from pepsin-digested atelocollagen isolated from porcine dermis and synthetic OCP. Two critical-sized defects (20 mm in diameter) were made in a dog skull. Ten disks of OCP/collagen or collagen (control) were implanted in the bone defects and resected with surrounding tissues at 3, 6, or 12 months after the implantation. The specimens were analyzed radiographically, crystallographically, histologically, and histomorphometrically. RESULTS: X-ray diffraction and FTIR analyses showed that OCP tended to convert to a poorly crystallized hydroxyapatite, similar to that of biological apatite, by 3 months. Radiographic and histologic analyses showed that the implantation of OCP/collagen disks initiated new bone formation in the defects at 3 months after implantation. However, there was no promotion of bone formation by control collagen disks even with prolonged implantation up to 12 months. Histomorphometric analysis revealed that the percentage of newly formed bone in the defect implanted with OCP/collagen increased significantly, from 30.91 ± 6.65 at 3 months to 51.22 ± 5.99 at 12 months, although the value tended to reach a plateau at 6 months (44.49 ± 3.34). On the other hand, the percentage of remaining OCP was estimated at approximately 10% at 3 months and remained nearly unchanged thereafter. CONCLUSION: The results suggest that bone regeneration of a critical-sized bone defect of dog calvaria by OCP/collagen can be enhanced for 3 to 6 months and that OCP/collagen holds potential as a bone substitute material.
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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 | 0.000 |
| 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) | 0.001 | 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".