Quantitative evaluation of connective tissue grafts on peri‐implant tissue morphology in the esthetic zone: A 1‐year prospective clinical study
Bibliographic record
Abstract
BACKGROUND: In the treatment of anterior implants, few studies have quantitatively evaluated the effects of connective tissue grafts on labial bone resorption and soft tissue recession. PURPOSE: To evaluate the influence of connective tissue grafting (CTG) on the peri-implant tissue morphology by quantitatively measuring change over time the tissue surrounding the implant in the anterior esthetic zone. MATERIAL AND METHODS: Twenty-six patients who received implants with platform shifting in the anterior esthetic region were included in this follow-up study. Patients were classified as those who received CTG [CTG (+) group] and those who did not [CTG (-) group]. The vertical and horizontal dimensions of the buccal alveolar bone of the implant and its surrounding soft tissues were evaluated using cone-beam computed tomography. RESULTS: At 1 year after connection of the superstructure, labial soft tissue recession was on average 0.64 mm in the CTG (-) group and 0.09 mm in the CTG (+) group, and this difference was significant (P < .001). Furthermore, mean labial bone resorption was 0.65 mm in the CTG (-) group and 0.13 mm in the CTG (+) group, and also this difference was significant (P = .003). CONCLUSIONS: Within the limitations of this study, these findings suggest that CTG may be effective in both reducing labial bone resorption around the implant and reducing the recession of the soft tissue.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".