Environmental exposures during perinatal development: methods of risk examination including chemical measurement for environmental tobacco smoke exposure, and meta-analysis and risk assessment for organic solvent exposure
Bibliographic record
Abstract
Two common environmental exposures during the perinatal period include environmental tobacco smoke (ETS) and organic solvents. Three different methods to examine perinatal risk after these environmental exposures were performed. These included a chemical measurement of a toxin in lung tissue for ETS exposure, and both a meta-analysis and a risk assessment for organic solvent exposure. The risk of Sudden Infant Death Syndrome (SIDS) has been shown to be associated with ETS exposure. Lung tissue concentrations of nicotine and cotinine were measured in SIDS (n = 44) and non-SIDS (n = 29) cases by radioimmunoassay. There were significantly higher concentrations of nicotine in the SIDS group compared to the non-SIDS group, p = 0.0001. Upon stratifying for reported smoking status, there were higher nicotine concentrations in SIDS cases for infants of both smoking and non-smoking caregivers when compared to non-SIDS cases. These results further support the relationship between ETS and the risk of SIDS, previously based on history and not as a biochemical test. An updated meta-analysis arrived at an overall quantitative estimate of the effect of organic solvent exposure on pregnancy outcome (N = 8845 patients for major malformation outcome). Solvents were associated with an increased risk for major malformations (odds ratio = 1.73, 95% confidence interval 1.42–2.11). The meta-analysis was unable to stratify for solvent class as this information was not available in the accepted literature. The updated meta-analysis further supports the results of the initial meta-analysis. To quantify potential fetal risks in a specific petroleum industry, a literature review was conducted of chemicals to which women working at Imperial Oil Limited (IOL) were exposed. Solvent exposures in these papers were compared to IOL exposure indices. In total, 559 studies were obtained from the literature search related to the solvents used in a refinery. Fifteen studies explicitly stated a chemical exposure level. Overall, IOL chemical exposures were lower than those reported in the literature to be associated with fetal risks. This approach allows for the mitigation of the misperception of teratogenic risk. It is recommended to minimize both infants' exposures to ETS and women's occupational exposure to solvents during pregnancy.
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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 | 0.050 | 0.084 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.055 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".