Microbiota associated with peri‐implantitis—A systematic review with meta‐analyses
Bibliographic record
Abstract
AIM: To answer the following PECO question: "In systemically healthy human subjects (P), which are the differences between peri-implantitis (E) and peri-implant health/mucositis (C) in terms of bacterial presence/count (O)?" MATERIALS AND METHODS: Cross-sectional studies fulfilling specific inclusion criteria established to answer the PECO question were included. Two review authors independently searched for studies, screened the titles and abstracts, did full-text analysis, extracted the data from the included reports, and performed the risk of bias assessment through an adaptation of the Newcastle/Ottawa tool for cross-sectional studies and of the JBI critical appraisal checklist. In case of disagreement, a third reviewer author took the final decision. Study results were summarized using random effects meta-analyses. RESULTS: A total of 12 studies were included, involving 1233 participants and 1513 implants. Peri-implantitis was associated with the presence of S. epidermidis (Odds ratio, OR = 10.28 [95% Confidence interval, CI: 1.26-83.98]), F. nucleatum (OR = 7.83 [95% CI: 2.24-27.36]), T. denticola (OR = 6.11 [95% CI: 2.72-13.76]), T. forsythia (OR = 4.25 [95% CI: 1.71-10.57]), P. intermedia (OR = 3.79 [95% CI: 1.07-13.35]), and P. gingivalis (OR = 2.46 [95% CI: 1.21-5.00]). Conversely, the presence of A. actinomycetemcomitans (OR = 3.82 [95% CI: 0.59-24.68]), S. aureus (OR = 1.05 [95% CI: 0.06-17.08]), and C. rectus (OR = 1.48 [95% CI: 0.69-3.17]) was not associated with peri-implantitis. CONCLUSIONS: Peri-implantitis is associated with the presence of S. epidermidis and specific periodontopathogens (P. gingivalis, T. forsythia, T. denticola, F. nucleatum, and P. intermedia). (CRD42021254589).
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".