The Composition of Alcohol Products from Markets in Lithuania and Hungary, and Potential Health Consequences: A Pilot Study
Bibliographic record
Abstract
AIMS: The rates of alcohol-attributable mortality in Lithuania and Hungary have been shown to be higher than those in most other European countries. Quality of alcohol products is investigated as a possible explanation. METHODS: In a descriptive pilot study, a convenience sample of alcohol products was collected from local city markets in both countries (Lithuania n = 10, Hungary n = 15) and chemical analyses, including some that have not been done in prior studies, were conducted. The parameters studied were alcoholic strength, volatiles (methanol, acetaldehyde, higher alcohols), ethyl carbamate, anions (including nitrate) and inorganic elements (including lead). Additionally, a multi-target screening analysis for toxicologically relevant substances was conducted. RESULTS: The majority of samples (64%) had an alcohol content between 35% vol. and 40% vol., being in accordance with the typical strength of legal spirits in Europe. Three samples containing significantly higher concentrations of alcohol above 60% vol. were found to be unrecorded alcohol products, defined as any alcohol that is outside of legal and taxed production. Screening analysis showed that those samples contained various flavourings, including the hepatotoxic substance coumarin, at concentrations above the legal limit for foods. All other substance classes under study were found to be at levels of no toxicological concern. CONCLUSIONS: Although some problems with the quality of the alcohol samples were found, there is insufficient evidence from this pilot study to conclude that alcohol quality has an influence on health as reflected in alcohol-attributable mortality rates. Given the extent of alcohol-attributable disease burden in central and eastern European countries, future research should focus on collection of large, representative samples, particularly of unrecorded sources, which was the most problematic product group in our study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".