The Effectiveness of a Customized Titanium Mesh for Ridge Preservation with Immediate Implantation in Dogs
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the effect of a newly designed titanium mesh (TM) for preserving the buccal bone around an immediately placed implant following tooth extraction in dogs. MATERIALS AND METHODS: Immediate implant placements were performed bilaterally in the mesial socket of the fourth premolar in five dogs. In one site, the TM was affixed to the fixture using its own stabilization components (TM group), and the contralateral site was left untreated (control group). All surgical sites were intended to be submerged with primary flap closure. Histologic and histomorphometric analyses were performed 16 weeks postoperatively. RESULTS: All implants were histologically osseointegrated, and buccal bone resorption was evident in both groups with the high rate of TM exposure (4/5). The most coronal level of bone-implant contact and the bone crest were not statistically different between the TM and the control group. A dense connective tissue layer consistently predominated under the TM, where mineralized tissue was not observed, and the vascularity and cellularity were minimal. CONCLUSIONS: It can be conjectured that preservation of buccal plate by using the TM in immediate implantation was not predictable due to vulnerability to wound dehiscence and substantial pseudoperiosteum formation beneath the TM.
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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 | 0.001 |
| 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".