Bone regeneration using titanium plate stabilization for the treatment of peri‐implant bone defects: A retrospective radiologic pilot study
Bibliographic record
Abstract
AIM: To 3-dimensional radiographically assess the effect of titanium plate in guided bone regeneration (GBR) for the treatment of peri-implant ridge defects in esthetic zone. MATERIAL AND METHODS: Nineteen patients with buccal peri-implant defects in the maxillary esthetic zone were treated with GBR using xenograft, autogenous bone, and collagen membrane. Subjects were divided into two groups: control (conventional GBR, 10 patients with 16 implants) and test (GBR with an adjunctive titanium plate; nine patients with 15 implants). Cone-beam computed tomography (CBCT) images obtained immediately after and 5-7 months following GBR were used to assess buccal crestal bone level (BBL) and buccal bone thickness (BBT) at different implant levels. RESULTS: Thirty-one implants in 19 patients were evaluated. Titanium plate exposure occurred in three cases (33.33%) of the test group. After 5-7 months, the mean BBL was located 1.48 ± 0.71 mm coronal to the platform in the test group and 0.90 ± 3.03 mm coronal to the platform in the control group (p = 0.03). The mean over all BBT (BBT-M) was 4.16 ± 0.48 mm in the test group and 2.38 ± 0.97 mm in the control group (p < 0.01). More resorption occurred in the control group than in the test group regarding mean BBL (3.00 ± 3.11 mm vs. 0.78 ± 0.79 mm, respectively; p = 0.04), BBT-M change (1.87 ± 1.59 mm vs. 0.56 ± 0.33 mm, respectively; p = 0.02), and percentage change in BBT-M (40.69 ± 24.01% vs. 11.53 ± 5.86%, respectively; p < 0.01). CONCLUSION: In the short-term, titanium plate-enhanced GBR maintained ridge dimensions better than conventional GBR did.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".