The Long Goodbye: Finally Moving on from the Relative Potency Approach to a Mixtures Approach for Polycyclic Aromatic Hydrocarbons (PAHs)
Bibliographic record
Abstract
For the past several decades, a relative potency approach has been used to estimate the human health risks from exposure to polycyclic aromatic hydrocarbon (PAH) mixtures. Risk estimates are derived using potency equivalence factors (PEFs; also called relative potency factors [RPFs]), based on the ratio of selected PAHs to benzo[a]pyrene (BaP), expressed qualitatively by orders of magnitude. To quantify PEFs for 18 selected carcinogenic PAHs, a systematic approach with a priori and dose response criteria was developed, building on draft work by the US EPA in 2010 and its review by US EPA Science Advisory Board (SAB) in 2011. An exhaustive search for carcinogenicity studies that included both target PAHs and BaP with environmentally relevant exposure routes found only 48 animal bioassay datasets (mostly pre-1992 based on skin painting). Only eight datasets provided adequate low-response data, and of these only four datasets were appropriate for modeling to estimate PEFs; only benzo[b]fluoranthene and cyclopenta[c,d]pyrene had a PEF that could be quantified. Thus, current knowledge of PAH carcinogenicity is insufficient to support quantitative PEFs for PAH mixtures. This highlights the long-acknowledged need for an interdisciplinary approach to estimate risks from PAH mixtures. Use of alternative and short-term toxicity testing methods, improved mixture characterization, understanding the fate and bioavailability of PAH mixtures, and understanding exposure route-related differences in carcinogenicity are discussed as ways to improve the understanding of the risks of PAHs.
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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 | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".