Selenomonas spp. and Periodontal Diseases: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Objective: To assess the association of Selenomonas species with periodontitis through a systematic review. Materials and Methods: A search was conducted to identify articles published between January 1, 2000, and September 2, 2024, in English and French, in the electronic databases PubMed, Scopus, and Web of Science. The inclusion criteria were human studies, including patients with periodontitis aged 18 years or older, and non-interventional cross-sectional, case-control, and cohort studies. Exclusion criteria were smoking, chronic systemic diseases, and pregnancy. The risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS). Results: Among the 6226 identified studies, only six met the inclusion criteria and were appropriate for answering the research question. They found a higher prevalence of Selenomonas species in patients with periodontitis than in healthy subjects. Additionally, two species, S. sputigena, and S. noxia, were frequently isolated from subgingival plaque and were associated with periodontal status. Conclusion: S. sputigena and S. noxia have been associated with periodontitis, including both aggressive and chronic forms, and may be considered putative pathogens in the disease. However, no robust evidence can be set since heterogeneous protocols were used in the included studies. Therefore, more research is necessary to refine this association.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".