Environmental Exposure to Dioxins, Dibenzofurans, Bisphenol A, and Phthalates in Children with and without Autism Spectrum Disorder Living near the Gulf of Mexico
Bibliographic record
Abstract
Environmental exposure to organic endocrine disrupting chemicals, including dioxins, dibenzofurans, bisphenol A (BPA), and phthalates has been associated with neurodevelopmental disorders, including autism spectrum disorder (ASD). We conducted a pilot monitoring study of 30 ASD cases and 10 typically developing (TD) controls ages 2–8 years from communities along the Gulf of Mexico near Alabama, which houses 14 Superfund sites, to assess the concentrations of dioxins and dibenzofurans in serum, and BPA and phthalate ester metabolites in urine. Based on General Linear Models, the lipid- or creatinine-adjusted geometric mean concentrations of the aforementioned chemicals did not differ between the ASD case and TD control groups (all p ≥ 0.27). We compared our findings to the adjusted means as reported by the National Health and Nutrition Examination Survey, survey years 2011–2012, and found that TD controls in our study had lower BPA (59%) and MEHHP (26%) concentrations, higher MBP (50%) concentration, and comparable (<20% difference) MEP, MBZP, MEOHP, and MCPP concentrations. We also conducted a preliminary investigation of dietary exposures and found that the consumption of certain types of fish may be associated with higher OCDD concentrations, and the consumption of soft drinks and juices may be associated with lower BPA and MEOHP concentrations, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".