Hair Analysis of Fatty Acid Ethyl Esters in the Detection of Excessive Drinking in the Context of Fetal Alcohol Spectrum Disorders
Bibliographic record
Abstract
A serious challenge in diagnosing fetal alcohol spectrum disorder (FASD) is the need to document alcohol use during pregnancy. Maternal/paternal alcohol abuse affects the likelihood of fetal alcohol exposure, and hence the occurrence of FASD. The objective of the current study was to document the use of the fatty acid ethyl ester (FAEE) hair test, a biomarker of excessive alcohol use, in parents at risk of having children with FASD and quantify the prevalence of alcohol use in this population. Hair samples submitted for FAEE testing between October 2005 and May 2007 were evaluated (n = 324). Subjects consisted of the parents of at-risk children. Samples were analyzed using a previously published method. Briefly, samples underwent a liquid-liquid extraction, followed by headspace solid phase microextraction, and were then analyzed by gas chromatography-mass spectrometry using deuterated FAEE as internal standards. Limit of detection and limit of quantification values were between 0.01-0.04 ng/mg and 0.04-0.12 ng/mg, respectively. Positive levels for excessive drinking were ascertained using a cutoff level of 0.5 ng/mg, offering 90% sensitivity and specificity. The rate of positive hair samples for excessive drinking was 33.3% (32.4% among women and 35.4% among men) (n = 324). The majority of samples (62%) had cumulative FAEE levels above a level that excludes strict abstinence (0.2 ng/mg) and many (19%) were highly positive (above 1.0 ng/mg). Of 26 FAEE hair tests for which women were reported to be pregnant, 38% had FAEE hair levels above 0.2 ng/mg and 19% tested positive for excessive drinking, with levels above 0.5 ng/mg; 12% had levels above 1.0 ng/mg. The high rate of positive FAEE results demonstrates that the FAEE hair test corroborates the clinical suspicion of alcohol use in parents of children at risk for FASD. Our results suggest that FAEE hair analysis may be a powerful tool in detecting excessive alcohol use in the perinatal period.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".