Titanium as a modifier of the peri‐implant microbiome structure
Bibliographic record
Abstract
BACKGROUND: Recent data support the implication of accelerated titanium dissolution products in peri-implantitis. It is unknown whether these dissolution products have an effect on the peri-implant microbiome, the target of existing peri-implantitis therapies. PURPOSE: This study assessed the relationship between the peri-implant microbiome, dissolved titanium levels, and peri-implantitis. MATERIALS AND METHODS: Clinical, microbiome, and titanium data were collected from a periodontal population having implants in function for 10 years. Clinical examinations were performed, and submucosal plaque samples were collected from the deepest site per implant. An aliquot of the sample was used for 16S rRNA gene sequencing, with the remainder analyzed for titanium quantity using mass spectrometry. Sequences were clustered into taxonomic units at 97% minimum sequence similarity using the QIIME pipeline approach. RESULTS: Fifteen implants were assessed. According to established case definitions, six had a diagnosis of peri-implantitis; nine were healthy. The genera Streptococcus, Prevotella and Haemophilus characterized peri-implant health. Peri-implantitis was associated with a marked increase in Veillonella. Quantities of dissolved titanium were identified in 40% of sites. Titanium presence was associated with peri-implant disease status (P = .02) and correlated to the first principal component of the microbiome (rho = 0.552) and its alpha-diversity (rho = -0.496). Canonical correlation analyses found that titanium levels, but not health or disease status of the implant, were significantly associated with the microbiota composition (P = .045). CONCLUSIONS: These findings suggest an association between titanium dissolution products and peri-implantitis and support a role for these products in modifying the peri-implant microbiome structure and diversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".