The human health effects of unconventional oil and gas (UOG) chemical exposures: a scoping review of the toxicological literature
Bibliographic record
Abstract
Abstract Many chemicals associated with unconventional oil and natural gas (UOG) are known toxicants, leading to health concerns about the effects of UOG. Our objective was to conduct a scoping review of the toxicological literature to assess the effects of UOG chemical exposures in models relevant to human health. We searched databases for primary research studies published in English or French between January 2000 and June 2023 on UOG-related toxicology studies. Two reviewers independently screened abstracts and full texts to determine inclusion. Seventeen studies met our study inclusion criteria. Nine studies used solely in vitro models, while six conducted their investigation solely in animal models. Two studies incorporated both types of models. Most studies used real water samples impacted by UOG or lab-made mixtures of UOG chemicals to expose their models. Most in vitro models used human cells in monocultures, while all animal studies were conducted in rodents. All studies detected significant deleterious effects associated with exposure to UOG chemicals or samples, including endocrine disruption, carcinogenicity, behavioral changes and metabolic alterations. Given the plausibility of causal relationships between UOG chemicals and adverse health outcomes highlighted in this review, future risk assessment studies should focus on measuring exposure to UOG chemicals in human populations.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".