The Bone Tissue Responses to Prehydrated and Collagenated Cortico‐Cancellous Porcine Bone Grafts: A Study in Rabbit Maxillary Defects
Bibliographic record
Abstract
BACKGROUND: Bone substitutes should have osteoconductive properties and be completely replaced with new bone with time. Adding collagen gel to prehydrated and collagenated porcine bone (PCPB) particles results in a sticky and moldable material which facilitates clinical handling. However, the possible influence of the gel on the bone tissue response is not known. PURPOSE: The objective of the study was to evaluate the bone tissue responses to PCPB graft with or without collagen gel and to evaluate the resorption/degradation properties of the biomaterials. MATERIALS AND METHODS: Fourteen rabbits were used in the study. Bilateral bone defects, 5 x 8 x 3 mm, were created in the maxilla and filled with PCPB + collagen gel (test) or with PCPB only (control) and covered with a collagen membrane. Animals were killed after 2 (n = 3), 4 (n = 3), and 8 weeks (n = 8) for histological and morphometrical evaluations. RESULTS: There were no differences between test and control defects. Both materials showed bone formation directly on the particles by typical osteoblastic seams. The bone area increased with time (2-8 weeks) for both sides, from 16.2% (control) and 19.2% (test) to 42.7 and 43.8%, respectively. The PCPB, whether mixed with collagen gel or not, was resorbed by osteoclasts as well as part of remodeling with the formation of osteons within the particles. Morphometry showed a decrease of PCPB area from 19.4% (control) and 23.8% (test) after 2 weeks to 3.7 and 9.3% after 8 weeks, respectively. CONCLUSIONS: Mixing collagen gel and PCPB to facilitate the clinical handling does not influence the bone tissue responses to the material, which exhibited osteoconductive properties and was resorbed with time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".