Canada-Wide Distribution of Environmental and Occupational Risk Factors for Urinary Stone Disease: Insights for Equitable Resource Allocation and Fighting Health Disparities
Bibliographic record
Abstract
Abstract: Objectives: This study aimed to map the distribution of nephrolithiasis’ environmental risk factors (occupational heat and heavy metal exposure and ambient seasonal temperature) and to assess the correlations of these exposures with the best estimates of the reported nephrolithiasis incidence in Canada. Methods: The regional average heat burden was defined as the mean temperature in the hottest three months of the year for 2020, 2021, and 2022. The employment rates in the top five industries with occupational heavy metal (cadmium, lead, and arsenic) and heat exposure were obtained from the Statistics Canada 2021 database. Statistical significance was calculated based on the 95% confidence interval difference from the null hypothesis. Correlation analysis was performed between our rates of nephrolithiasis risk factors and previously published estimates of the stone incidence: kidney stone interventions and acute kidney stone event rates. Results: Lower-latitude provinces had higher overall mean temperatures in 2020 to 2022, with Ontario, Manitoba, and Prince Edward Island having the highest seasonal heat burdens, in this order. Nunavut had the lowest rate of occupational heat exposure, while the remaining regions had similar rates. Yukon, the Northwest Territories, and Nunavut had significantly higher rates of occupational heavy metal exposure compared to the remaining regions. The ambient temperature and occupation heavy metal and heat exposure showed no significant correlation with the estimates of the stone incidence. Conclusions: The occupational heat exposure was relatively similar between regions. Northern Canada had higher occupational heavy metal exposure compared to other regions. Occupational exposures and temperature variations were not associated with the nephrolithiasis incidence in Canada.
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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 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".