Differences of Bone Regeneration by Various Calcium Phosphate/Collagen Composites
Bibliographic record
Abstract
Our previous studies showed that synthetic octacalcium phosphate (OCP) enhances bone regeneration more than hydroxyapatite (HA) and β-tricalcium phosphate (β-TCP). Also, a synthetic bone substitute constructed of synthetic octacalcium phosphate (OCP) and porcine atelocollagen sponge (OCP/Col) showed stable bone regeneration. The present study was designed to investigate the difference of bone regeneration by OCP/Col and other calcium phosphate-collagen composites. OCP/Col, β-TCP$-collagen% composite (β-TCP/Col), or HA$-collagen% composite (HA/Col) sponge was prepared from pepsin-digested atelocollagen isolated from the porcine dermis and OCP, β-TCP, or HA granules, respectively. A standardized critical-sized defect was made in the rat calvarium, and various calcium phosphate-collagen composites were implanted into the defect. The rats were fixed at four weeks after implantation and radiographic and histological examinations were performed by undecalcified cross sections of implants. Radiographic examination showed that uniform radiopaque masses were observed in the created defects treated with OCP/Col, whereas granulous and foggy radiopacity was observed in β-TCP/Col and HA/Col. Histological examination showed that newly formed bone was observed in the reticulum of OCP/Col and around the implanted OCP. The regenerated bone by β-TCP/Col or HA/Col seemed to be less than that by OCP/Col and would not to be nucleated by the granules of β-TCP or HA. The present study indicated that bone regeneration by OCP/Col was different from those of β-TCP/Col and HA/Col. Application of OCP/Col would be expected for clinical use in the future.
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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".