Risk assessment of lung cancer related to environmental PAH pollution sources
Bibliographic record
Abstract
We assessed the lung cancer risk in six localities with aluminium smelting activities and five with other polycyclic aromatic hydrocarbon (PAH) pollution sources, using two quantitative risk assessment (QRA) approaches for PAH mixtures and compared their risk predictions against actual cancer incidence. In the first approach, carcinogen exposure was estimated from animal-derived BaP toxic equivalents (BaPeq) of individual PAHs. The upper bound lifetime risk estimates ranged between 0.012-4.7 x 10(-5) and 0.019-0.94 x 10(-5) in the aluminium and other localities, respectively. The second approach assumed that the potency of PAH mixtures was linked to their BaP content and lifetime lung cancer unit risk gradients were estimated from epidemiological studies based on BaP exposure measurements. Lifetime risks ranged between 0.02-89 x 10(-5) and 0.06-6.8 x 10(-5) in the aluminium and other localities, respectively. Predicted risks were generally higher in smelter towns, and higher when based on epidemiological studies than on BaPeq. In smelting communities, there was a linear relationship (R2 approximately 0.8) between female lung cancer rates and PAH exposure estimates. To conclude, animal/BaPeq-based QRAs predicted lower risks than occupational/BaP-based QRAs. Epidemiological validation of the QRA could be performed for elevated past exposure to PAHs, but not for currently lower concentrations.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".