Decreasing the Mouth Alcohol Effect by Increasing the Salivary Flow Rate
Bibliographic record
Abstract
The retention of alcohol in the oral cavity (the mouth alcohol effect) is a major limitation of breath alcohol testing and requires a wait of 15 to 20 minutes. Currently the only method to reduce this effect is to rinse the mouth with water. In this study, the mouth alcohol effect was found to be substantially decreased by increasing the salivary flow rate. Nineteen female and 11 male alcohol-free subjects on two occasions rinsed their mouths with 20 mL of diluted vodka (20% v/v) for 20 seconds and then expectorated. The subjects kept their mouths closed and provided breath samples into an Intoxilyzer® 5000C five and ten minutes after expectoration with and without chewing one piece of sugar-free gum (a salivary flow promoter). The subjects chewed the gum for five minutes, then removed the gum and provided the breath samples. On the other occasion, the subjects did not chew gum. The mean Intoxilyzer® results (± Standard Error of the Mean (SEM)) after 5 minutes were 0.155 (± 0.012) g/210 L with no gum and 0.022 (± 0.003) g/210 L after chewing gum. Chewing gum caused a mean percent decrease in the BrAC due to the mouth alcohol effect of 85 (± 1.6)% after five minutes. After 10 minutes an Intoxilyzer® result > 0.010 g/210 L was found in 27 subjects (90%) when they did not chew gum, compared to none (0%) when gum was chewed. Increasing the salivary flow rate causes a large reduction in the magnitude and duration of the mouth alcohol effect. The use of salivary flow promoters may allow for more rapid breath alcohol testing after the last consumption of alcohol.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".