Bibliographic record
Abstract
PURPOSE OF REVIEW: Air pollution has been increasingly associated with diverse adverse health outcomes, including airway diseases. Data suggest that gene-environment interactions are important in this context. However, evidence regarding causal effects of exposure and development of allergic conditions specifically remains immature. We review the developments of the past 18 months regarding air pollution, genetics and epigenetics, and allergy. RECENT FINDINGS: Conflicting evidence for air pollution as causative in the development of allergic disease persists. However, recent data support the associations between long-term exposure to traffic-related pollutants and newly developed sensitization in children. Studies from India and China demonstrate the global burden of health-related costs attributed to air pollutants and allergic diseases. The effect of exposure seems to be modified by coexposures of allergens as well as genetic variants, particularly those moderating response to oxidative stress. Potential links between exposures and epigenetic (DNA methylation) changes with consequences for disease development are also reinforced. SUMMARY: Data over the past 18 months support prior literature that air pollutants cause exacerbation, and possibly onset, of allergic disease. Regarding the onset of asthma specifically, the evidence of causality has grown significantly, but it remains difficult to separate allergic from nonallergic asthma. Effect of modification by genetic variants and epigenetic changes warrants further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".