Ethical and Philosophical Dimensions of Applying Biomarkers to Pregnant Women
Bibliographic record
Abstract
In not every case of exposure do we know the clinical/health implications for the new-born. This raises questions of the state of the science as well as that of potentially causing undue alarm among women, whether the exposures are voluntary or involuntary. The ethical principles of intergenerational justice and of doing no harm underscore a role for environmental epidemiologists in prenatal testing. There are limitations in the methods leading to ethical challenges for communicating risks observed because early science requires replication and corroboration of a body of evidence. Better characterization of health risks of in utero exposures to multiple chemicals is needed to inform efforts to reduce prenatal exposures. Mediation analysis can be a mechanism to distinguish between an environmental chemical exposure and preterm birth. Pregnancy is a critical period for impacting the adult life of the fetus; decisions about levels of exposure and thresholds need to be addressed taking ethical implications into consideration. What level of hydroxylated metabolite 4-OH-PCB 107 contributes to lower birth-weight among smokers, and does acrylamide have an impact prenatally through diet or smoking? These are some of the questions that will impact health care provider decisions or the behavior of pregnant mothers. Are there critical developmental windows of exposure during pregnancy that would impact IQ? What biomarkers are valid and reliable for determining such exposure and making informed decisions? The ethical dimensions of these considerations will be drawn upon to engage the audience and facilitate discussion.
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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 | 0.304 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.093 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.022 | 0.038 |
| 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".