Investigating the oral microbiome in health and periodontal disease
Bibliographic record
Abstract
16S rRNA was used to determine the microbiome associated with health and chronic periodontitis (CP). We hypothesized that a comparison of plaque in health and disease will help identify CP-associated bacteria to develop novel diagnostics for CP. NGS was done against the V3 hypervariable region of the 16S rRNA gene and sequences were clustered based on 97% similarity. Taxonomic assignment and distance measures were used to assess bacterial composition. We identified disease indicators: Filifactor alocis, Synergistes, Tanerella forsythia and TM7 taxa in SupG and SubG sites. Health indicators included Rothia dentocariosa, TM7_7BB428, Selenomonas noxia, Fusobacteriales and Campylobacter. Surprisingly, `classic' periodontal pathogens could be isolated from the tongue in CP patients, which may provide a novel sampling site for prognostic tests. We have identified known periodontal pathogens, including F. alocis, a strong indicator of CP, which could be included as a novel member in the Red complex of periodontal pathogens.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".