The fatty acid ethyl esters (FAEE) hair test: emerging technology for the diagnosis of fetal alcohol spectrum disorders (FASD)
Bibliographic record
Abstract
Aim: Fetal alcohol spectrum disorder (FASD) is the most prevalent cause of neurocognitive handicap among North American children.A serious challenge in the diagnosis of FASD is the need to document excessive maternal drinking during pregnancy, however maternal self-report is often unreliable creating a need for an objective biomarker.Methodology: Testing for xenobiotics in hair has been gaining popularity in recent years as a screening method for drug use because of its unique advantage of being non-invasive and providing a stable, long-term record of past and or chronic drug exposure.The recent advent of a hair test to measure excessive alcohol use, the FAEE hair test, has opened the door to exploring its use as a new diagnostic tool for FASD.The current article briefly reviews recent advances in research involving the FAEE hair test in this context.Conclusion: Recent advances in research involving the FAEE hair test suggest that FAEE hair analysis may be a powerful tool in detecting heavy alcohol use in the perinatal period and in FASD diagnosis. Key words: Foetal alcoholism
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".