Defining a Lowest Observable Adverse Effect Hair Concentrations of Mercury for Neurodevelopmental Effects of Prenatal Methylmercury Exposure Through Maternal Fish Consumption: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Methylmercury is an environmental pollutant that can cause irreversible effects on the development of children. Although there is no doubt that high exposure can cause neurodevelopmental deficits, the threshold that will adversely affect the developing fetus is not well defined. Our objective was to systematically review the evidence of neurodevelopmental risks of methylmercury to the unborn child from maternal fish consumption to define the lowest observable adverse effect hair concentration (LOAEHC). METHODS: A systematic review was conducted of all original research reporting on the effects of methylmercury on the human fetus. A literature search was undertaken using SCOPUS, Medline-Ovid, PubMed, Google Scholar, and EMBASE. Papers were selected based on the following inclusion criteria: 1) child neurodevelopmental outcome; 2) comparison groups; and 3) methylmercury exposure through fish consumption. RESULTS: Forty-eight publications met these inclusion criteria. Thirty articles reported on longitudinal studies and 18 were cross-sectional studies. Variations in study design precluded formal meta-analysis. Based on an evaluation of these studies, we defined the LOAEHC at 0.3 microg/g of maternal hair mercury. The longitudinal studies yielded a LOAEHC of 0.5 microg/g. CONCLUSION: In the clinical context, the majority of pregnant women consume mercury-containing fish in amounts that are lower than the LOAEHC defined in this study. However, the LOAEHC is in the same order of magnitude of mercury exposure that occurs in significant numbers of women. Hence, although it appears safe to suggest that eating the recommended types and amounts of fish poses no measurable risks for neurodevelopmental deficits, analysis of hair mercury content before pregnancy might be suggested because dietary modification can decrease body content and risk.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".