Influence of Periodontal Biotype on Buccal Bone Remodeling after Tooth Extraction Using the Flapless Approach with a Xenograft: A Histomorphometric and Fluorescence Study in Small Dogs
Bibliographic record
Abstract
BACKGROUND: Several approaches have been used to counteract alveolar bone resorption after tooth extraction. PURPOSE: The aim of the present study was to evaluate the influence of gingival thickness and bone grafting on buccal bone remodeling in extraction sockets with thin buccal bone, using a flapless approach. MATERIALS AND METHODS: The gingiva of 8 dogs was thinned at one side of the mandible and mandibular premolars were extracted without flaps. The sockets were randomly assigned to the test group (thin gingiva) (TG), the test group with grafting material TG + GM, the control group (normal gingiva) (CG), or the control group with grafting material CG + GM. Ground sections were prepared from 12-week healing biopsies, and histomorphometry and fluorescence analysis were performed. RESULTS: In the groups with thin gingiva, numerically greater buccal bone loss was observed, while there were no differences between grafted and nongrafted sites. A numerically higher rate of mineralization was observed for the grafted sites, as compared with the nongrafted sites, at 12 weeks. CONCLUSIONS: A thin buccal bone plate leads to higher bone loss in extraction sockets, even with flapless surgery. The gingival thickness or the use of a graft material did not prevent buccal bone resorption in a naturally thin biotype, but modified the mineralization process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 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".