Hazardous alcohol use: a cross-sectional study of cardiology patients in Sweden
Bibliographic record
Abstract
BACKGROUND: Alcohol use is understudied in cardiology settings. We investigated the prevalence of hazardous alcohol use and probable dependence among cardiology patients. METHODS: Cross-sectional study in three regions of Sweden. Alcohol use was assessed using the AUDIT-10 questionnaire. We defined hazardous alcohol use as: AUDIT-10 ≥ 6 for women or ≥ 8 for men (primary definition) and probable dependence as AUDIT-10 ≥ 13 for women or ≥ 15 for men. We examined associations using logistic regression. RESULTS: We included 1107 participants (median age = 73 years; range = 18-102; 66% men). The prevalence of hazardous alcohol use was 7.8% (95%CI = 6.2-9.3, primary definition) and 0.9% (95%CI = 0.3-1.5) had probable alcohol dependence. We found increased odds of hazardous alcohol use in: age groups 18-39 years (OR = 4.90, 95%CI = 1.41-17.08) and 40-64 years (OR = 4.02, 95%CI = 1.69-9.67) compared to ≥80 years; a city compared to a small town (OR = 2.44, 95%CI = 1.02-5.84); participants with unhealthy diets (OR = 2.37, 95%CI = 1.36-4.13), and overweight participants (OR = 2.25, 95%CI = 1.23-4.12). CONCLUSIONS: Hazardous alcohol use affected about one in 12 cardiology patients. However, less than 1 in 100 had probable alcohol dependence. Findings suggest that many cardiology patients with hazardous alcohol use are appropriate for brief interventions, and may not require more intensive alcohol dependence treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".