What Do Late-life Polychlorinated Biphenyls (PCB) Levels Tell us on Lifetime Internal Exposure? An Evaluation of Exposure Misclassification in Retrospective Breast Cancer Epidemiologic Studies
Bibliographic record
Abstract
PP-29-063 Background/Aims: Despite the experimental evidence of polychlorinated biphenyls (PCB) carcinogenicity, epidemiologic studies on the association between PCB exposure and breast cancer remain mostly inconclusive. These studies may be hampered by the fact that the PCB levels are measured at the time of diagnosis or a few years before and might not represent internal exposure during hypothesized periods of susceptibility like puberty. We conducted this study to evaluate PCB exposure misclassification in a large sample of French women, using a published physiologically based pharmacokinetic model for persistent organic pollutants. Methods: Lifetime PCB-153 pharmacokinetic profiles were backtracked using a physiologically based pharmacokinetic framework that integrates information on women age, height profile, weight changes across time, pregnancies, and breast-feeding history. Environmental exposure was optimized to match simulated blood concentration with levels measured at the age of diagnosis (28–75 years) while taking temporal trends of contamination into account. We performed analyzes with 2 different half-lives (10 and 30 years) as values reported in the literature vary. The area under the curve of blood concentration for each 10-year interval and maximum blood concentration (Cmax) were then compared to measured levels with following 2 approaches: (i) quartile classification in different age categories and (ii) correlation analyzes. Results: We found a quartile misclassification varying from 10% to 75%, early decades of life being the ones displaying the highest discrepancy with quartiles based on late-life PCB levels. Measured concentrations and simulated maximum blood concentration were correlated with coefficients of r = 0.88 and 0.81 (Spearman rank correlation) for half-lives of 10 and 30 years, respectively. Conclusion: Assuming that backtracked levels were accurate, these results suggest that classification based on PCB levels measured at the time of diagnosis does not adequately reflect exposure during earlier hypothesized windows of susceptibility. Hence, caution should be exercised when interpreting results from breast cancer epidemiologic studies.
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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 | 0.054 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".