Predictions of Blood Ethanol Levels Resulting From Occupational Use of Hydro Alcoholic Solutions and Ethanol-Based Varnishes
Bibliographic record
Abstract
The purpose of this study was to produce data on the ethanol concentrations in ambient air that result from hand rubbings with hydroalcoholic solutions (HAS) or the use of ethanol-based varnishes, and then to predict the blood ethanol levels (BELs) that result from these procedures. The concentration of ethanol in air at the volunteer’s nose after the application of HAS on hands was measured with five volunteers who performed five tests in two different environments: 1) in an inhalation chamber (air change rate ~18 h-1), and 2) in a closed office (poorly ventilated) with two different amounts (1.5 and 3 g) of HAS. In the case of varnish, 125 ml were applied on a 1-m 2 wood surface placed in the middle of an inhalation chamber (n=4). The ethanol concentration was measured 20 cm and 40 cm from the center of the board for the next 60 minutes. As for HAS we noted a large intra- and inter-individual variability in ethanol levels in inhaled air. As expected the highest concentration in the inhalation chamber (~1250 ppm) was lower than in the office (~2352 ppm). For the application of the varnish, the ethanol concentrations greatly exceeded 1000 ppm for a short duration (< 4 min). Physiologically-based pharmacokinetic (PBPK) modeling of ethanol concentrations based on ethanol levels measured in inhaled air predicted the following maximum BELs in women (men): 0.39 and 0.37 mg/L (0.37 and 0.35 mg/L) in the office, and 0.26 and 0.42 mg/L (0.25 and 0.40 mg/L ) in the inhalation chamber for 1.5 g and 3 g, respectively. The total dose of ethanol absorbed estimated for a working day involving 42 hand rubbings with 1.5 or 3 g of HAS averaged 0.20 g. For the varnish, the predicted highest BELs for men and women were 0.77 and 0.79 mg/L, respectively. In all cases, the BELs remained below 1 mg/L. The results of this study should make it easier to assess the risk related to chronic inhalation of low levels of ethanol in the general population and among workers associated with these practices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".