Childhood Leukemia in Small Geographical Areas and Proximity to Industrial Sources of Air Pollutants in Three Colombian Cities
Bibliographic record
Abstract
Acute leukemia is the most common childhood cancer and has been associated with exposure to environmental carcinogens. This study aimed to identify clusters of acute childhood leukemia (ACL) cases and analyze their relationship with proximity to industrial sources of air pollution in three capital cities in Colombia during 2000-2015. Incident ACL cases were obtained from the population cancer registries for the cities of Bucaramanga, Cali, and Medellín. The inventory of industrial sources of emissions to the air was obtained from the regional environmental authorities and industrial conglomerates were identified. The Kulldorf's circular scan test was used to detect city clusters and to identify clusters around industrial conglomerates. Multivariable spatial modeling assessed the effect of distance and direction from the industrial conglomerates controlling for socioeconomic status. We identified industrials sectors within a buffer of 1 km around industrial conglomerates related to the ACL clusters. Incidence rates showed geographical heterogeneity with low spatial autocorrelation within cities. The spatio-temporal tests identified one cluster in each city. The industries located within 1 km around the ACL clusters identified in the three cities represent different sectors. Exposure to air pollution from industrial sources might be contributing to the incidence of ACL cases in urban settings in Colombia.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".