Effect of customized healing abutments on the peri‐implant linear and volumetric tissue changes at maxillary immediate implant sites: A 1‐year prospective randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Immediate implant placement (IIP) associated with the use of bone substitutes and collagen matrices (CM) seems to reduce the amount of resorption at peri-implant areas. Recently, customized healing abutments (CA) appeared as another solution in order to seal the socket and preserve the original soft tissue contour. PURPOSE: To evaluate peri-implant tissues dimensional changes after using customized healing abutments compared with the use of xenogeneic collagen matrices as socket sealing options in flapless maxillary immediate implant placement. MATERIAL AND METHODS: The present study was designed as a prospective, randomized, controlled clinical trial. Patients were allocated into two groups depending on the socket sealing option: in the CM group a collagen matrix was used and in the CA group a customized abutment. Digital impressions were taken prior to extraction, 1, 4, and 12 months after implant insertion and the digital files allowed to evaluate linear buccal changes (MBC) and the buccal volumetric variation (BVv) between the different time points at peri-implant tissue areas. Additionally, mucosa variation was computed assessing the papilla presence and the midfacial mucosa height. Statistical significance was set at 0.05. RESULTS: Twenty-eight patients were observed during a 12-month period. Significant differences between mean values of BVv at the first month were observed at the CM and CA group (-9.75 ± 6.65% and -4.76 ± 5.29%, respectively) (p = 0.043). At the 1-year follow-up, no significant differences were found in terms of BVv between the two groups, although the thin bone phenotype (≤1 mm) significantly influenced the volumetric variations that occurred in each group. No significant differences were noticed in midfacial mucosa and papillae alteration between groups, after 1 year of treatment. CONCLUSION: Both treatment options are predictable solutions for socket sealing in IIP, although a higher volumetric variation can be expected in the presence of thin bone phenotypes.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".