Assessment of urbanization, air pollution and water pollution as environmental risk factors of amyotrophic lateral sclerosis in New Brunswick
Bibliographic record
Abstract
Background: Amyotrophic lateral sclerosis (ALS) is a rare neurological disease resulting in progressive loss of voluntary muscle control. While genetic factors have been identified, the role of environmental influence in ALS development remains uncertain. Thus, we created a robust database from multiple sources to investigate the association between long-term exposure at place of residence to various local climate zones (urbanization), air pollutants, and proximity to water bodies with indicators of run-off sources and the development of ALS. Methods: A matched case-control study was conducted in New Brunswick (NB), Canada from January 2003 to February 2021. ALS cases were individually matched with respect to sex and year of birth with four randomly selected controls. Study population included 304 ALS patients and 1207 controls with their historical postal codes from the NB Citizen Database linked to spatial environmental datasets from the Canadian Urban Environmental Health Research Consortium to compare their environmental exposures prior to onset via conditional logistic regression models. Results: For urbanization, odds of ALS were not significantly associated with the local climate zone classification categories, nor for exposure to greenness, in the adjusted regression models. For air pollution, odds of ALS were significantly associated with increased SO2 exposure (Odds ratio, 95% confidence interval = 1.23, 1.02-1.47, per 0.14 ppb increase of SO2) in adjusted models, but not associated with the other studied air pollutants. For water pollution, the final fitted models suggested no association between proximity to water bodies with indicators of potential run-off sources and the development of ALS. Conclusions: Greater exposure to SO2, even at intensities below the Canadian Ambient Air Quality Standards green management levels, was associated with an increased risk of developing ALS. Further studies addressing replicating these results and addressing limitations of this study are needed.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 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".