Factors Affecting the Implant Supracrestal Complex: A Scoping Review as Part of a Global Consensus Meeting Organised by the Osstem Implant Community<b></b>
Bibliographic record
Abstract
Aim: To map current evidence and identify key factors influencing peri-implant tissue stability related to abutment configuration, design, materials, and prosthetic protocols in implant-supported fixed prostheses. Methods: A comprehensive search of PubMed and Scopus was performed up to June 2025, supplemented by manual searches. Human studies in English with ≥1 year of follow-up were included. Two reviewers independently conducted screening, data extraction, and quality assessment using the Newcastle–Ottawa Scale. The review was carried out at the Universities of Sassari and Ferrara in collaboration with the Osstem Global Consensus Meeting. Results: From 974 records, 46 studies were included: 33 randomized clinical trials, 9 cohort, 2 case-control, and 2 cross-sectional studies. Thirty-nine were rated as good quality and five as fair. Concave abutment profiles and emergence angles < 30° promoted peri-implant tissue stability, while convex designs and wider angles increased risks of bone loss and peri-implantitis. Titanium remains the reference abutment material in posterior sites, zirconia provides superior aesthetics anteriorly, and hybrid abutments balance strength and esthetics. Conclusions: Prosthetic design and abutment material selection critically affect peri-implant tissue stability and esthetic outcomes. Evidence supports screw-retained designs, platform switching, and the “one abutment–one time” approach for predictable long-term success.
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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 | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".