The primacy of octacalcium phosphate collagen composites in bone regeneration
Bibliographic record
Abstract
We have engineered a scaffold constructed of synthetic octacalcium phosphate (OCP) and porcine collagen sponge (OCP/Col), and reported that OCP/Col drastically enhanced bone regeneration. In this study, we investigated whether OCP/Col would enhance bone regeneration more than beta-tricalcium phosphate (beta-TCP) collagen composite (beta-TCP/Col) or hydroxyapatite (HA) collagen composite (HA/Col). Discs of OCP/Col, beta-TCP/Col, or HA/Col were implanted into critical-sized defects in rat crania and fixed at 4 or 12 weeks after implantation. The newly formed bone and the remaining granules of implants in the defect were determined by histomorphometrical analysis, and radiographic and histological examinations were performed. Statistical analysis showed that the newly formed bone by the implantation of OCP/Col was significantly more than that of beta-TCP/Col or HA/Col. In contrast, the remaining granules in OCP/Col were significantly lower than those in beta-TCP/Col or HA/Col. Bone regeneration by OCP/Col was based on secured calcified collagen and bone nucleation by OCP, whereas bone regeneration by beta-TCP/Col or HA/Col was initiated by poorly calcified collagen and osteoconductivity by beta-TCP or HA. This study showed that the implantation of OCP/Col in a rat cranial defect enhanced more bone regeneration than beta-TCP/Col and HA/Col.
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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) | 0.000 | 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 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".