Linear Versus Non-Linear Dose-Response Relationship Between Prenatal Alcohol Exposure and Meconium Concentration of Nine Different Fatty Acid Ethyl Esters
Bibliographic record
Abstract
Presence of individual fatty acid ethyl esters (FAEEs) in meconium is considered to be a reliable biomarker of prenatal alcohol exposure, and their concentration has been found to be linearly associated with poor postnatal development, supporting the widely extended idea that ethanol is a non-threshold teratogen. However, a growing number of epidemiological studies have consistently found a lack of adverse short- and long-term fetal outcomes at low exposure levels. We therefore aimed to investigate the relationship between the concentration of individual FAEEs and prenatal alcohol exposure in meconium samples collected within the first 6 to 12?h after birth from 182 babies born to abstainer mothers and from 54 babies born to women who self-reported either light or moderate alcohol ingestion in the second or third trimester of pregnancy. In most cases, the individual FAEE concentrations were negligible and not significantly different (P >0.05) between exposed and control babies. The concentrations appeared to increase linearly with the dose only in the few babies born to mothers who reported >3 drinks/week. These results provide evidence that the correlation between prenatal alcohol exposure and individual FAEE concentrations in meconium is non-linear shape, with a threshold probably at 3 drinks/week.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".