Self‐reported alcohol use versus phosphatidylethanol in behavioral trials: A study of people living with <scp>HIV</scp> in Tshwane, South Africa
Bibliographic record
Abstract
BACKGROUND: Accurately quantifying alcohol use among persons with HIV (PWH) is important for validly assessing the efficacy of alcohol reduction interventions. METHODS: We used data from a randomized controlled trial of an intervention to reduce alcohol use among PWH who were receiving antiretroviral therapy in Tshwane, South Africa. We calculated agreement between self-reported hazardous alcohol use measured by the Alcohol Use Disorders Identification Test (AUDIT; score ≥8) and AUDIT-Consumption (AUDIT-C; score ≥3 for females and ≥4 for males), heavy episodic drinking (HED) in the past 30 days, and heavy drinking in the past 7 days with a gold standard biomarker--phosphatidylethanol (PEth) level (≥50 ng/mL)--among 309 participants. We used multiple logistic regression to assess whether underreporting of hazardous drinking (AUDIT-C vs. PEth) differed by sex, study arm, and assessment time point. RESULTS: Participants' mean age was 40.6 years, 43% were males, and 48% were in the intervention arm. At 6 months, 51% had PEth ≥50 ng/mL, 38% and 76% had scores indicative of hazardous drinking on the AUDIT and AUDIT-C, respectively, 11% reported past 30-day HED, and 13% reported past 7-day heavy drinking. At 6 months, there was low agreement between AUDIT-C scores and past 7-day heavy drinking relative to PEth ≥50 (sensitivities of 83% and 20% and negative predictive values of 62% and 51%, respectively). Underreporting of hazardous drinking at 6 months was associated with sex (OR = 3.504. 95% CI: 1.080 to 11.364), with odds of underreporting being greater for females. CONCLUSIONS: Steps should be taken to decrease underreporting of alcohol use in clinical trials.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".