Cause-specific Mortality in Patients Treated for Alcohol Use Disorders in State-Run Services in Novosibirsk, Russia
Bibliographic record
Abstract
Aims: To analyze disparities in age at death and cause-specific mortality in a sample of patients registered with alcohol use disorders (AUDs) in state-run addiction treatment centers in Novosibirsk, Russia. Methods: Database: 92,269 deaths recorded by medical facilities in Novosibirsk between 2000 and 2010, comprising cause of death (per ICD-10), sex, and date of birth and death. Average age at death and proportion of cause-specific deaths were compared between patients (n =1,762) treated for AUDs as a primary diagnosis and the general population, the latter derived from deaths recorded by all medical facilities.Results: The average age at death was significantly lower (p < .001) in patients compared with the general population; men lived, on average, 8.4 years fewer; for women, this difference was 19.7 years. The pronounced gender gap in age at death in the general population (12.7 years) disappeared in the patient sample. They incurred proportionally more deaths because of infectious diseases, injuries, poisonings, diseases of the digestive system, and certain cardiovascular diseases such as cardiomyopathies. They incurred proportionally fewer deaths due to chronic ischemic heart disease, myocardial infarction, cerebrovascular diseases, and neoplasms.Conclusions: Compared to the general population, cause-specific mortality in AUD patients was high in categories largely contributing to a premature death. Specific measures including screenings for alcohol problems in primary health care and early interventions to reduce level of drinking should be a priority.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".