Assessing human polychlorinated biphenyl contamination for epidemiologic studies: lessons from patterns of congener concentrations in Canadians in 1992.
Bibliographic record
Abstract
Humans are always exposed to mixtures of polychlorinated biphenyls (PCBs), so assessment of their health effects is complicated. Because the original sources are relatively standard mixtures that change in predictable ways while traversing the environment, there is substantial uniformity in the congener mixtures people carry. To the extent that concentrations are highly correlated, measuring multiple congeners within correlated groups would be unnecessary and estimation of separate biologic effects would be impossible. We examined correlation patterns in previously collected data on 38 congeners (and 14 other organochlorines) from 497 human milk samples from Canada from 1992. Congeners 138, 153, 156, 157, 170, 183, 187, 194, 199, and 203 were highly intercorrelated; 180 had slightly lower correlations with this group. Congeners 74, 105, and 118 were highly intercorrelated and moderately to highly correlated with the first group. Congener 99 had moderate correlations with both these groups, and congener 66 had lesser correlations with the primary group. In contrast, congeners 28, 44, 49, 60, 90/101, 128, 137, and 193 showed little correlation with any other congeners. The remaining 14 congeners were uninformative; they were quantified in fewer than 30% of samples, and varying lipid concentrations meant that those quantified were not necessarily at higher concentrations than those not quantified. In study of human health effects of PCBs, the congener pattern present in the population under study should be examined when deciding which congeners to measure; instead of solely redundant or uninformative congeners, attention should be given to other congeners that may be more useful in addressing the question of interest.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".