Alarming Prevalence of Fetal Alcohol Exposure in a Mediterranean City
Bibliographic record
Abstract
The prevalence of gestational ethanol exposure and subsequent fetal exposure has been assessed in a cohort of mother-infant dyads in a Mediterranean city (Barcelona, Spain) by meconium analysis of fatty acid ethyl esters (FAEEs) after showing in this population a high prevalence of meconium opiates (8.7%), cocaine (4.4%), and cannabis (5.3%). Of the 353 meconium samples analyzed for FAEEs, 159 (45%) contained a total amount of seven FAEEs equal or above 2 nmol/g meconium, the cutoff internationally accepted to differentiate heavy maternal alcohol consumption during pregnancy from occasional use or no use at all. No parental sociodemographic differences or maternal features differentiated exposed from unexposed newborns. The prevalence of gestational consumption of ethanol was similar between women using and not using drugs of abuse during pregnancy (45.7% and 44.7% of samples with total FAEEs equal or higher than 2 nmol/g meconium, respectively). Meconium samples from newborns exposed in utero to ethanol, and positive for at least one illicit drug (cocaine, opiates, or cannabis), had total FAEEs and five of nine individual FAEEs statistically higher than the meconium samples that were negative for the most frequently used illicit drugs of abuse. Among the most prevalent FAEEs, oleic acid ethyl ester showed the best correlation to total FAEE concentration followed by palmitoleic acid ethyl ester . This study, which highlights a 45% ethanol consumption during pregnancy in a low socioeconomic status cohort, may serve as an eye opener for Europeans that gestational alcohol exposure is not endemic only in areas outside of Europe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".