Cluster of Bacteria Associated with Peri‐Implantitis
Bibliographic record
Abstract
BACKGROUND: Information on the microbiota in peri-implantitis is limited. We hypothesized that neither gender nor a history of periodontitis/smoking or the microbiota at implants differ by implant status. MATERIALS AND METHODS: Baseline microbiological samples collected at one implant in each of 166 participants with peri-implantitis and from 47 individuals with a healthy implant were collected and analyzed by DNA-DNA checkerboard hybridization (78 species). Clinical and radiographic data defined implant status. RESULTS: Nineteen bacterial species were found at higher counts from implants with peri-implantitis including Aggregatibacter actinomycetemcomitans, Campylobacter gracilis, Campylobacter rectus, Campylobacter showae, Helicobacter pylori, Haemophilus influenzae, Porphyromonas gingivalis, Staphylococcus aureus, Staphylococcus anaerobius, Streptococcus intermedius, Streptococcus mitis, Tannerella forsythia, Treponema denticola, and Treponema socranskii (p < .001). Receiver operating characteristic curve analysis identified T. forsythia, P. gingivalis, T. socranskii, Staph. aureus, Staph. anaerobius, Strep. intermedius, and Strep. mitis in peri-implantitis comprising 30% of the total microbiota. When adjusted for gender (not significant [NS]), smoking status (NS), older age (p = .003), periodontitis history (p < .01), and T. forsythia (likelihood ratio 3.6, 95% confidence interval 1.4, 9.1, p = .007) were associated with peri-implantitis. CONCLUSION: A cluster of bacteria including T. forsythia and Staph. aureus are associated with peri-implantitis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".