Effects of Smoking and Treatment Status on Periodontal Bacteria: Evidence That Smoking Influences Control of Periodontal Bacteria at the Mucosal Surface of the Gingival Crevice
Bibliographic record
Abstract
BACKGROUND: We examined whether smoking status could influence growth of potentially pathogenic bacteria in the periodontal environment of treated and untreated periodontal patients. METHODS: We have previously reported effects of treatment status on marker bacteria in our patients. We established a history of any smoking during 6 months prior to microbiological sampling (F-ME, 16 smokers out of 64; MHM, 70 smokers out of 185). We used a commercial immunoassay to quantitate Porphyromonas gingivalis, Prevotella intermedia, and Actinobacillus actinomycetemcomitans in paper point samples from periodontal sites. RESULTS: Logistic regression showed that in smokers, neither P. gingivalis nor A. actinomycetemcomitans was quantitatively increased, while P intermedia was somewhat increased. Multiple regression demonstrated that smoking disrupts the positive relationship between increasing probing depth and increasing bacterial growth that is found in non-smokers. In smokers, growth of marker bacteria at shallow sites (< or =5 mm) was significantly increased to the levels found at deeper sites (>5 mm) in both smokers and non-smokers. Supragingival plaque biofilm was identified as a reservoir for marker bacteria; smokers and nonsmokers had equal ranges of oral cleanliness. CONCLUSIONS: Smoking-associated periodontitis is not simply a reflection of oral cleanliness. Smoking extends a favorable habitat for bacteria such as P. gingivalis, P. intermedia, and A. actinomycetemcomitans to shallow sites (< or =5 mm). Molecular byproducts of smoking interfere with mechanisms that normally contain growth of damaging bacteria at the surface of the oral mucosa in gingival crevices. In this way, smoking can promote early development of periodontal lesions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".