Microbial communities of titanium versus zirconia abutments on implant‐supported restorations: Biodiversity composition and its impact on clinical parameters over a 3‐year longitudinal prospective study
Bibliographic record
Abstract
BACKGROUND: Shifts in microbial communities are common over time, but they may disturb the host-microbiome homeostasis and result in inflammation of the peri-implant issues if a dysbiotic biofilm is established. PURPOSE: Considering that different oral substrate surfaces may have a relevant impact on the microbial adhesion and colonization, the aim of this study was to investigate the microbial communities of the biofilm formed on single-implant restorations using titanium or zirconia abutments and how they correlate with clinical parameters after 3-years of implant loading. MATERIALS AND METHODS: MiSeq sequencing of 16S rRNA amplicons was used to characterize the oral biofilms of individuals (n = 20) who were sampled longitudinally during 3 years of masticatory loading. Bioinformatics analysis and multivariate statistical analysis were used to evaluate the microbial diversity and clinical outcomes. RESULTS: Microbiomes of both abutment materials presented high alpha-diversity indices during all the experimental period, irrespective of the time of sampling. Microbial communities of titanium and zirconia were quite different over time, differing about 30% after 3 years of functional loading. Similarity of microbiomes between tested abutments and contralateral teeth was also low, ranging between 45% and 50% after 3 years of investigation. Periodontal pathogens commonly associated with peri-implantitis were found in both groups. Furthermore, both abutment materials presented strong correlations of diversity indices and microbial taxa with clinical outcomes. CONCLUSIONS: The type of abutment substrate significantly influenced diversity and clustering of communities during 3 years of functional loading. The time of sampling had no effect on the variables. Large correlations were found between microbial findings and clinical outcomes.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".