Comments and Reflections on Ethics in Screening for Biomarkers of Prenatal Alcohol Exposure
Bibliographic record
Abstract
Early identification of and intervention for fetal alcohol spectrum disorder (FASD) has been shown to optimize outcomes for affected individuals. Detecting biomarkers of prenatal alcohol exposure (PAE) in neonates may assist in the identification of children at risk of FASD enabling targeted early interventions. Despite these potential benefits, complicated ethical issues arise in screening for biomarkers of PAE and these must be addressed prior to the implementation of screening programs. Here, we identify and comment, based on a North American perspective, on concerns raised in the current ethical, social, and legal literature related to meconium screening for PAE. Major ethical concerns revolve around the targeting of populations for PAE screening, consent and respect for persons, stigma and participation rates, the cost-benefit analysis of a screening program, consequences of false-positive and false-negative test results, confidentiality and appropriate follow-up to positive screen results, and the use of screen results for criminal prosecution. We identify gaps in the literature on screening for PAE, most notably related to a lack of stakeholder perspectives (e.g., parents, healthcare providers) about screening and the ethical challenges it presents.
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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 | 0.069 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.016 | 0.023 |
| 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".