Alcohol and Mortality: Combining Self-Reported (AUDIT-C) and Biomarker Detected (PEth) Alcohol Measures Among HIV Infected and Uninfected
Bibliographic record
Abstract
BACKGROUND: Unhealthy alcohol use may be particularly detrimental among individuals living with HIV and/or hepatitis C virus (HCV), and is often under-reported. Direct biomarkers of alcohol exposure may facilitate improved detection of alcohol use. METHODS: We evaluated the association of alcohol exposure determined by both self-report [Alcohol Use Disorders Identification Test-Consumption (AUDIT-C)] and a direct biomarker [phosphatidylethanol (PEth)], with mortality among HIV-infected and HIV-uninfected in the Veterans Aging Cohort Study-Biomarker Cohort. We considered PEth <8 ng/mL to represent no alcohol use. Alcohol exposure by AUDIT-C scores [0, 1-3/1-2 (men/women), 4-7/3-7 (men/women), 8-12] and PEth (<8, ≥8) was combined into categories to model the relationship of alcohol with mortality. Participants were followed from blood collection date for 5 years or until death within 5 years. RESULTS: The sample included 2344 (1513 HIV+; 831 uninfected) individuals, 95% men. During a median follow-up of 5 years, 13% died. Overall, 36% were infected with HCV (40% HIV+/HCV+, 27% HIV-/HCV+). Overall, 43% (1015/2344) had AUDIT-C = 0 (abstinence). Of these, 15% (149/1015) had PEth ≥8 suggesting recent alcohol exposure. Among those with AUDIT-C = 0, HCV+ individuals were more likely to have PEth ≥8. After controlling for age, sex, race, HIV, HCV, and HIV viral suppression, those with AUDIT-C = 0 but PEth ≥8 had the highest risk of mortality (adjusted hazard ratio 2.15, 95% confidence interval: 1.40 to 3.29). CONCLUSIONS: PEth in addition to self-report may improve detection of alcohol use in clinical settings, particularly among those at increased risk of harm from alcohol use. Individuals infected with HCV were more likely to under-report alcohol use.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".