The analysis of oral air using selected ion flow tube mass spectrometry in persons with and without a history of oral malodour
Bibliographic record
Abstract
OBJECTIVES: Oral malodour is a common disorder predominantly caused by bacterial metabolism of food stuffs in the mouth. It is routinely diagnosed and monitored by either the subjective rating or the measurement of oral volatile sulphur compound (VSC) levels. Non-sulphur compounds are also believed to contribute significantly to the condition although there is currently no direct means to assess their levels. In this study, we utilized selective flow tube mass spectrometry (SIFT-MS) to measure, in real time, a range of sulphur and non-sulphur containing compounds in oral air to determine whether the technique can be used to objectively monitor oral malodour. METHODS: Oral malodour was assessed using organoleptic scores in subjects with and without a history of oral malodour (n = 18) by a trained rater, while the chemical composition of oral air was analysed by both VSC sensor and SIFT-MS. RESULTS: Total VSC levels were significantly correlated with levels of hydrogen sulphide and methylmercaptan measured by SIFT-MS, but not with organoleptic scores. In subjects with elevated organoleptic score, only levels of methylmercaptan were significantly elevated. In three subjects with elevated tongue organoleptic scores but normal total VSC levels, SIFT-MS suggested that one subject possessed high levels of oral acetone while another had high oral levels of acetic acid. CONCLUSIONS: Our data suggest that SIFT-MS can be used to assess a wide range of compounds in oral air in addition to VSC to provide a clearer picture of the chemical nature of malodour. This may assist in the diagnosis and monitoring of the condition.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".