Collagen membranes at immediate implants: a histomorphometric study in dogs
Bibliographic record
Abstract
Abstract Aim: To evaluate the influence of resorbable membranes on hard tissue alterations and osseointegration at implants placed into extraction sockets in a dog model. Material and methods: In the mandibular premolar region, implants were installed immediately into the extraction sockets of six Labrador dogs. Collagen‐resorbable membranes were placed at the test sites, while the control sites were left uncovered. Implants were intended to heal in a submerged mode. After 4 months of healing, the animals were sacrificed, and ground sections were obtained for histomorphometric evaluation. Results: After 4 months of healing, a control implant was not integrated (n=5). Both at the test and at the control sites, bone resorption occurred. While the most coronal bone‐to‐implant contact was similar between the test and the control sites, the alveolar bone crest outline was maintained to a higher degree at the buccal aspect of the test sites (loss: 1.7 mm) compared with the control sites (loss: 2.2 mm). Conclusions: The use of collagen‐resorbable membranes at implants immediately placed into extraction sockets contributed to a partial (23%) preservation of the buccal outline of the alveolar process. To cite this article: Caneva M, Botticelli D, Salata LA, Souza SLS, Carvalho Cardoso L, Lang NP. Collagen membranes at immediate implants: a histomorphometric study in dogs. Clin. Oral Impl. Res. 21, 2010; 891–897. doi: 10.1111/j.1600‐0501.2010.01946.x
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".