ASSOCIATIONS OF LUNG CANCER MORTALITY WITH LONG-TERM EXPOSURE TO PM <sub>2.5</sub> COMPONENTS
Bibliographic record
Abstract
Background and Aims: Numerous epidemiological studies have now documented that long-term exposure to fine particulate matter air pollution mass (PM2.5) is associated with an increased risk of mortality. The ACS study has found associations of PM2.5 with increased risk of lung cancer mortality, but the types of particles that are most related to these associations have not been investigated. The focus of this new research was to determine which components of PM2.5 mass were most explanatory of the previously reported PM2.5 association with lung cancer mortality. Methods: Using the ACS cohort (extended through 2004), and the U.S. EPA PM2.5 Speciation data, we evaluated mortality associations between various composition and source components of PM2.5 in 100 U.S. metropolitan areas. Source apportionments were conducted using methods by Thurston and Spengler (1982). Individual elements were also considered as exposure indices. Mortality analyses employed Cox Proportional Hazards modeling. Results: The major U.S. PM2.5 sources identified, their key tracer elements, and their mean nationwide PM2.5 impacts were: Metals (Pb, Zn) 0.2 ug/m3; Soil (Ca, Si) 0.8 ug/m3; Traffic (OC, EC, NO2) 4.6 ug/m3; Steel (Fe, Mn) <0.1 ug/m3; Coal Combustion (As, Se, S) 1.1 ug/m3; Oil Combustion (V, Ni) 0.9 ug/m3; Salt (Na, Cl) 0.1 ug/m3; Biomass burning 1.3 ug/m3; Other Sulfates (S) 4.3 ug/m3; Other Nitrates (NO3-) 0.6 ug/m3; and, Other Organic Carbon (OC) 0.6 ug/m3. Coal combustion-related PM2.5 and its key trace elements were most strongly associated with lung cancer PM2.5-mortality associations. Conclusions: Particles resulting from the combustion of fossil fuels, especially coal, are most associated with increased risk of lung cancer mortality from long-term PM2.5 exposure. Acknowledgement: This research supported by the Health Effects Institute’s National Particle Component Toxicity Initiative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".