Relationship between halitosis and periodontal disease – associated oral bacteria in tongue coatings
Bibliographic record
Abstract
AIM: The objective of our study was to investigate the relationship between halitosis and oral bacteria in tongue coating (TC) and saliva samples from patients with halitosis, and to evaluate the effect of tongue cleaning on halitosis. METHODS: Ninety-four participants complaining of oral malodour were included in the study. Organoleptic (OR) values, volatile sulphur compound (VSC) concentrations determined by gas chromatography and TC scores were used as clinical parameters of halitosis. Quantitative real-time polymerase chain reactions were used to determine the numbers of periodontal disease-associated oral bacteria. RESULTS: There was a significant correlation between TC scores and OR values, methylmercaptan (CH3 SH) concentrations and VSC concentrations (Spearman's rank-correlation coefficient test, P < 0.01). There was also a positive correlation between the clinical parameters of halitosis and total bacterial numbers and Prevotella intermedia, Fusobacterium nucleatum and Campylobacter rectus concentrations in the TC samples. However, there was no similar correlation with respect to the saliva samples. The participants were sub-divided into two groups based on whether they had the habit of tongue cleaning or not. The participants with the habit of tongue cleaning had significantly lower OR scores, VSC concentrations and P. intermedia, F. nucleatum and C. rectus levels than the other participants (Mann-Whitney U-test, P < 0.05). CONCLUSION: These results suggested that periodontal disease-associated oral bacteria in TCs are closely related to halitosis and that tongue cleaning may be an effective method for improving halitosis.
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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 | 0.000 |
| 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".