The role of the oral microbiota and intrinsic host factors in peri-implant diseases: peri-implant mucositis versus peri-implantitis
Bibliographic record
Abstract
Aim: To characterize and compare the role of the oral microbiota and host biomarkers in patients with peri-implant mucositis and peri-implantitis. Methods: Patients diagnosed with either peri-implant mucositis (N=13) or peri-implantitis (N=20) were recruited from the Dr. Sam Borden Graduate Periodontics Clinic at the University of Manitoba. Subgingival plaque samples and crevicular fluid samples were collected from diseased implant sites and healthy natural teeth to study the local microbiome and host biomarker profiles, respectively. 16 rRNA and ITS rRNA amplicon sequencing were performed to detect bacterial and fungal species. Eight different cytokines were isolated and analyzed: IL-1β, IL-2, IL-4, IL-6, IL8, IL-10, IFN-γ, TNF-α. Results: Bacterial differential abundance analysis indicated statistically significant differences in the abundance of 17 different bacterial taxa and species between sites with peri-implant mucositis and peri-implantitis, most notably Tannerella forsythia was significantly more abundant at sites with peri-implantitis compared to sites with peri-implant mucositis. Fungal differential abundance analysis indicated that Malassezia species were significantly reduced in sites with peri-implant mucositis compared to sites with peri-implantitis. Cytokine analysis indicated no statistically significant differences in the crevicular concentrations of any cytokines analyzed between the peri-implant mucositis and peri-implantitis groups. Conclusion: Peri-implant mucositis and peri-implantitis have similar microbial communities and cytokine profiles; however, the presence of certain bacterial and fungal species may play a role in the transition between peri-implant diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".