Serum Dioxin Concentrations and Thyroid Hormone Levels in the Seveso Women's Health Study
Bibliographic record
Abstract
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental contaminant. Although experimental evidence suggests that TCDD alters thyroid hormone levels in rodents, human data are inconsistent. In 1976, a trichlorophenol plant exploded in Seveso, Italy. Women living in highly exposed areas were followed through the Seveso Women's Health Study. TCDD concentrations were measured in 1976 (n = 981) and 1996 (n = 260), and levels of total thyroxine, free thyroxine, free triiodothyronine, and thyroid-stimulating hormone were measured in 1996 (n = 909) and 2008 (n = 724). We used conditional multiple linear regression and marginal structural models with inverse-probability-of-treatment weights to evaluate associations and causal effects. TCDD concentration in 1976 was inversely associated with total thyroxine level in 1996 but not in 2008. Associations were stronger among women who had been exposed before menarche. Among these women, associations between total thyroxine and concurrent 1996 TCDD were slightly weaker than those with 1976 TCDD. A model including both 1976 and 1996 measurements strengthened the relationship between 1976 TCDD and total thyroxine but drove the association with 1996 TCDD to the null. TCDD exposure was not associated with levels of other thyroid hormones. TCDD exposure, particularly exposure before menarche, may have enduring impacts on women's total thyroxine levels. Initial exposure appears to be more influential than remaining body burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".