The prevalence of alcohol-related deaths in autopsies performed in Lithuania between 2017 and 2020: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Consumption of alcohol is a risk factor for non-communicable and infectious diseases, mental health problems, and can lead injuries and violence. The aim of this study was to evaluate the prevalence of alcohol-involved deaths among decedents who died of external causes and underwent autopsy in Lithuania. METHODS: Study includes age persons of any age (from 0 to 110 years) who died and were autopsied in Lithuania from 1 January 2017 to 31 December 2020. Data were obtained from the Lithuanian State Register of Deaths and Their Causes. RESULTS: Among external causes of death, the presence of alcohol was detected in 55.0% of cases. Male decedents had a significantly higher number of positive BAC level recorded, at 46.6%, compared with female decedents (32.1%; P < 0.001). The highest incidence of deaths where the alcohol was detected in the deceased's blood was found when the decedent was listed as being in the victims of assault group (71.5%, 95% CI 65.4-77.2). However, the highest median BAC score was found for those in the accidents group (59.7%, 95% CI: 58.2-61.2, BAC 2.42 ‰, IQR 1.86). CONCLUSIONS: The findings of this study suggest that alcohol use may be a contributing factor in a wide range of fatal incidents, including accidents, injuries, and cases of violent intent. Inequalities between males and females were identified, with a higher proportion of males with alcohol detected in blood at the time of death.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".