Predictors of Polychlorinated Biphenyl Concentrations in Adipose Tissue in a General Danish Population
Bibliographic record
Abstract
Polychlorinated biphenyls (PCBs) are ubiquitously present in the environment and suspected of carcinogenic, neurological, and immunological effects. Our purpose was to identify predictors of adipose tissue levels of mono-, di-, and tri-ortho-substituted PCBs experienced by a general population and to establish whether predictors vary according to substitution group. In this study of 245 randomly selected persons from a prospective Danish cohort of 57,053 persons, we examined geographical area, age, lactation, BMI, and intake of eight major dietary groups as potential determinants of adipose concentrations of mono-, di-, and tri-ortho-substituted PCBs by linear regression analyses. Lactation, BMI, and intake of fruit, vegetables, and dairy products showed negative associations with PCB concentrations in adipose tissue in all models, and living in Copenhagen city, age, and consumption of fish (particularly fatty fish) were positively associated. The associations between several of the predictors and mono-ortho-substituted PCBs tended to differ from the associations found for di- and tri-ortho-substituted PCBs. In conclusion, geography, age, lactation, BMI, and consumption of fatty fish consistently predicted the concentration of PCBs in adipose tissue. Our results indicate that predictors of PCBs varied according to substitution group, suggesting that ortho-substituted groups should be analyzed separately.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".