Health risks to infants from exposure to PCBs, PCDDs and PCDFs
Bibliographic record
Abstract
Global contamination by a variety of anthropogenic persistent organic chemicals, such as dioxins and PCBs, has resulted in human exposure throughout all phases of development. Detectable concentrations of PCBs and dioxins have been found in amniotic fluid, placenta and foetal tissue samples while infants who are breast-fed can obtain blood levels greater than those of their mother's. In two separate food poisoning episodes where infants were exposed in utero to elevated levels of heat-degraded PCBs (PCBs, PCQs, PCDFs), a variety of adverse mental and physical developmental abnormalities have been observed. In additional human cohorts where exposure could be considered as environmental or background, more subtle effects, including lower birth weights, alterations in thyroid hormones and lymphocyte subpopulations and detriments in neurological development, have been consistently seen. In most instances, negative associations were made between in utero exposure to contaminants compared with lactational. Although the observed neurodevelopmental deficits have been described as subtle, there could be unknown consequences related to future intellectual functionality. Current regulatory efforts should focus on identification and control of environment and food chain contamination as in utero exposure is a direct consequence of the accumulated maternal body burdens.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".