Do Gender, Education, and Income Modify the Effect of Air Pollution Gases on Cardiac Disease?
Bibliographic record
Abstract
OBJECTIVE: We sought to determine whether gender, education, and income influence the susceptibility to ambient air pollution. METHODS: We determined the association between daily cardiac hospitalizations and daily concentrations of gaseous air pollutants in 10 large Canadian cities using time-series analyses adjusted for day-of-the week, temperature, barometric pressure, relative humidity. RESULTS: Percentage increases in hospitalization associated with an increase in air pollution equivalent to its mean value were statistically significant for ozone, carbon monoxide and nitrogen dioxide individually (P < 0.05) and the combined pollutant effect was 8.5% (95% confidence interval: 1.8, 14.6). The air pollution-cardiac disease association was not significantly influenced by gender or community level of education or income. CONCLUSION: Short-term changes in air pollution may adversely affect cardiac disease but gender, and community education and income do not accurately identify those with increased susceptibility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".