Cocaethylene as a biomarker to predict heavy alcohol exposure among cocaine users.
Bibliographic record
Abstract
BACKGROUND: Cocaethylene (CE) is a cocaine metabolite formed during alcohol and cocaine co-consumption. There are no previous studies to assess the effectiveness of hair CE as a biomarker indicating chronic alcohol consumption among individuals who have consumed cocaine. OBJECTIVES: To establish the ability of CE to predict chronic alcohol use among individuals testing positive for cocaine. METHODS: We studied all cases referred to our laboratory where both chronic cocaine and alcohol consumption were sought, and values of hair cocaine, benzoylegconine (BE), CE, and FAEEs (as marker of chronic alcohol consumption ) were available. Cocaine, BE and CE were screened by ELISA and confirmed using headspace-solid phase microextraction (HS-SPME) and GC-MS. FAEE were analyzed using HS-SPME and GC-MS/EI. Sensitivity, specificity, and predictive values of CE as a marker of alcohol consumption among cocaine users were calculated using different FAEE cutoffs. RESULTS: Cocaine (P<0.001) and BE (P<0.001) concentrations were associated with increased FAEE. The positive predictive value of CE to identify alcohol consumption was 0.66 for excessive drinking and 0.76 for chronic drinking among positive cocaine users. Negative CE ruled out almost completely excessive alcohol consumption. CONCLUSION: Positive hair CE results had high specificity for chronic excessive alcohol consumption among cocaine users. With no established safe level of alcohol in pregnancy, identification of CE in hair of pregnant women who have used cocaine can serve as a biomarker for fetal alcohol spectrum disorder.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".