Validating Self‐Reported Unhealthy Alcohol Use With Phosphatidylethanol (PEth) Among Patients With HIV
Bibliographic record
Abstract
BACKGROUND: We sought to compare self-reported alcohol consumption using Timeline Followback (TLFB) to biomarker-based evidence of significant alcohol use (phosphatidylethanol [PEth] > 20 ng/ml). Using data from patients with HIV (PWH) entering a clinical trial, we asked whether TLFB could predict PEth > 20 ng/ml and assessed the magnitude of association between TLFB and PEth level. METHODS: We defined unhealthy alcohol use as any alcohol use in the presence of liver disease, at-risk drinking, or alcohol use disorder. Self-reported alcohol use obtained from TLFB interview was assessed as mean number of drinks/day and number of heavy drinking days over the past 21 days. Dried blood spot samples for PEth were collected at the interview. We used logistic regression to predict PEth > 20 ng/ml and Spearman correlation to quantify the association with PEth, both as a function of TLFB. RESULTS: Among 282 individuals (99% men) in the analytic sample, approximately two-thirds (69%) of individuals had PEth > 20 ng/ml. The proportion with PEth > 20 ng/ml increased with increasing levels of self-reported alcohol use; of the 190 patients with either at-risk drinking or alcohol use disorder based on self-report, 82% had PEth > 20 ng/ml. Discrimination was better with number of drinks per day than heavy drinking days (AUC: 0.80 [95% CI: 0.74 to 0.85] vs. 0.74 [95% CI: 0.68 to 0.80]). The number of drinks per day and PEth were significantly and positively correlated across all levels of alcohol use (Spearman's R ranged from 0.29 to 0.56, all p values < 0.01). CONCLUSIONS: In this sample of PWH entering a clinical trial, mean numbers of drinks per day discriminated individuals with evidence of significant alcohol use by PEth. PEth complements self-report to improve identification of self-reported unhealthy alcohol use among PWH.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".