Abstract 14197: Industrial Developmental Toxicant Emissions and Congenital Heart Disease in Urban and Rural Alberta, Canada
Bibliographic record
Abstract
Background: We have previously demonstrated a downward temporal association between mixtures of organic solvents released into air from industrial sources and congenital heart disease (CHD) in Alberta. Hypothesis: In the current study we hypothesized that the downward temporal associations between developmental toxicants (DTs) and CHD would have a different industrial sector participation in the urban and rural areas of Alberta. Methods: We extracted yearly emissions of DTs (tonnes) from Canada’s National Pollutant Release Inventory as released to air between 2003-2010. We identified at the postal code level, all CHD cases born between 2004-2011 through our provincial-wide echocardiography database. We calculated yearly urban and rural CHD crude rates. Urban and rural were defined according to Forward Sortation Areas. Principal Component Analysis was undertaken to reduce the dimensionality of the number of DTs and the yearly solution was applied to rural and urban areas to test correlations with corresponding yearly CHD rates. Results: Three principal components (PCs) were identified: PC1 consisted of a mixture of organics and gases, PC2 consisted of a mixture of organics only and PC3 consisted of metals. There were strong positive correlations between CHD rates and urban PC1 emissions from mining and manufacturing, (r=0.76, p=0.03; 0.74, p=0.04); whereas in rural areas, PC1 emissions from mining and utilities and PC2 emissions from mining and manufacturing were associated with rates of CHD, (r=0.71, p=0.05; r=0.74, p=0.04 and r=0.86, p=0.01; r=0.74, p=0.04, respectively). PC3 showed no positive correlations (Table 1). Conclusions: CHD rates are consistently strongly correlated with mixtures of organic compounds and gases, with different patterns of chemicals and sectors involved in the urban and rural settings. This provides an opportunity to further investigate spatial correlations between DTs and CHD rates at a higher resolution scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".