A Simulation of the Effect of Blood in the Mouth on Breath Alcohol Concentrations of Drinking Subjects
Bibliographic record
Abstract
After consuming lunch, twenty-six male subjects ingested alcohol ad libitum over approximately one hour. At least 1.5 hours after drinking ceased, the subjects provided breath samples into a Breathalyzer® Model 900 or 900A. Immediately after providing the breath samples, blood was collected from the cubital vein using a sterile disposable plastic syringe. Part of the blood sample (3–10 mL) was placed into a blood tube containing 1 % sodium fluoride and 0.5 % sodium citrate and was analysed for alcohol by headspace gas chromatography. The remaining blood (3–10 mL) was placed in the subject's mouth for up to 30 seconds and then swallowed or expectorated. A second Breathalyzer test was conducted within ten minutes of the first. The blood alcohol concentrations of the subjects averaged 0.095 g/dL and ranged between 0.044 to 0.168 g/dL. The untruncated Breathalyzer results were significantly lower after introducing blood into the mouth (p=0.017). When these Breathalyzer results were truncated to two decimal places, however, these slight differences were eliminated. In addition, when the initial Breathalyzer results were compared to the blood alcohol concentrations the apparent blood breath ratios averaged 2319, with a range of 1947:1 to 2654:1. We conclude that blood in the mouth does not lead to an overestimation of the breath alcohol concentration of drinking subjects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".