Examination of sex differences in fatty acid ethyl ester and ethyl glucuronide hair analysis
Bibliographic record
Abstract
Clinical studies examining performance of fatty acid ethyl esters (FAEE) and ethyl glucuronide (EtG) in identifying excessive alcohol consumption have been primarily conducted in male populations. An impact of hair cosmetics in producing both false-negative EtG results and false-positive FAEE results has been demonstrated, suggesting a possible bias in female populations. This study evaluates FAEE-positive hair samples (>0.50 ng/mg) from n = 199 female and n = 73 male subjects for EtG. Higher FAEE/EtG concordance was observed amongst male over female subjects. Performance of multiple proposed EtG cut-off levels were assessed; amongst female samples, FAEE/EtG concordance was 36.2% (30 pg/mg), 36.7% (27 pg/mg), and 43.7% (20 pg/mg). Non-coloured hair demonstrated a two-fold increase in concordance (41.8 v. 20.8%) over coloured hair in the female cohort. FAEE levels did not differ between male and female subjects; however they were lower in coloured samples (p = 0.046). EtG was lower in female subjects (p = 0.019) and coloured samples (p = 0.026). A total of n = 111 female samples were discordant. Amongst discordant samples (EtG-negative), 26% had evidence of recent alcohol use including consultation histories (n = 20) and detectable cocaethylene (n = 9); 29% of discordant samples were coloured. False-negative risk with ethyl glucuronide analysis in females was mediated by cosmetic colouring. These findings suggest that combined analysis of FAEE and EtG is optimal when assessing a female population and an EtG cut-off of 20 pg/mg is warranted when using combined analysis. While concordant FAEE/EtG-positive findings constitute clear evidence, discordant FAEE/EtG findings should still be considered suggestive evidence of chronic excessive alcohol consumption.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".