Autogenous bone block grafting provides facial implant tissue stability long‐term
Bibliographic record
Abstract
BACKGROUND: Mucosal recession (MR) and bone loss can compromise anterior implant esthetics. PURPOSE: To evaluate tissue stability and clinical outcomes of anterior implants augmented with autogenous block transplants long-term. MATERIALS AND METHODS: This prospective cross-sectional clinical study analyzed facial tissue recession of anterior implants augmented with autogenous bone blocks and compared them to adjacent teeth in forty patients 52 months post-augmentation. Clinical parameters, MR and implant transparency, were assessed at delivery and follow-up. The hypothesis is that the facial mucosa of augmented implant sites is more resistant to trauma than the gingival margins of adjacent teeth. RESULTS: Teeth were seven times more likely to present a facial recession than adjacent augmented implants at 52-month follow-up (RR: 7; P < .001; 95%CI: 2.7-18.0). Augmented implant sites were six times more likely to present "no-tissue-recession" than adjacent teeth (RR: 6.2; P < .001; 95%CI: 2.4-15.7). Mean tooth facial tissue recession was significantly higher than adjacent implants, 1.18 ± 1.05 mm (range: 0-3.5 mm) vs. 0.06 ± 0.2 mm (95%CI: 0.8-1.5; P < .0001). Thick biotype teeth were 2 times more resistant to recession than thin biotype teeth (RR: 2.03; P = .03; 95%CI: 1.2-3.5). Implant success rates were 100%. Lack of transparency and MR at facial implant sites lasted an average of 52 months and up to 144 without signs of inflammation or pocket formation regardless of the individual's biotype. Facial bone thicknesses of 2.2 mm seem optimal for tissue stability. CONCLUSIONS: Autogenous bone block augmentation with staged implant placement seems to be a predictable, short-healing, reconstructive protocol in the esthetic zone maintaining stable peri-implant tissues long-term. Implant augmented sites seem more resistant to develop a recession than adjacent teeth.
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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) | 0.002 | 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".