Assessment of Particle Oxidative Potential as an Air Pollution Exposure Metric: A Systematic Review
Bibliographic record
Abstract
Ambient particulate air pollution is linked to numerous acute and chronic health outcomes. The standard approach to measuring particulate exposure has been to estimate particle mass concentration. New measures account for oxidative potential, the ability of particles to cause oxidative stress in the body. There has been no systematic comparison of particulate air pollution exposure assessment methods between those that incorporate oxidative potential and those that do not. The aim of this review is to determine if associations between particulate air pollution exposure and health outcomes are stronger when oxidative potential is used as the exposure metric, in comparison to particle mass concentration. The databases Medline and Embase were searched from inception to 28 March 2018 for studies that reported an association between particulate air pollution exposure and a health outcome in a human population, and in which exposure was measured by both particle mass concentration and oxidative potential. Study quality was assessed using a US National Toxicology Program instrument. We identified 18 publications meeting the selection criteria. The most common oxidative potential assay was antioxidant depletion in synthetic respiratory tract lining fluid, used in 12 articles. The rate of consumption of dithiothreitol was also frequently used as an oxidative potential assay and appeared in 5 studies. The dithiothreitol assay most consistently produced stronger effect estimates, as 10/14 endpoints showed higher point estimates of associations compared to particle mass concentration estimates. Other assays showed mixed results. To date there is no consistent evidence that oxidative potential is more strongly associated with health outcomes than particle mass concentrations; however, these assays do have the advantage of not treating all particles as equally harmful and further work should explore which specific assays are most relevant to evaluating air pollution health risks.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".