An exploratory study of chemical elements in drinking water and non-Hodgkin's lymphoma
Bibliographic record
Abstract
Previous studies reported associations of non-Hodgkin's lymphoma (NHL) with agriculture, agricultural practices, herbicide, and insecticide exposure. McDuffie et al. (McDuffie, H.H., P. Pahwa, and D.F. White. 1995. Saskatchewan women and agricultural exposures: any relationship to tumor incidence? In Agricultural Health and Safety: Workplace, Environment, Sustainability, ed. HH McDuffie, JA Dosman, KM Semchuk, S.A. Olenchock, and A. Senthilselvan, 135–42. Boca Raton, FL: CRC Press, Lewis Publishers) demonstrated a significantly higher risk of NHL associated with drinking water from shallow as compared to deep wells among women in Saskatchewan. Contamination of drinking water derived from groundwater sources may be due to natural sources and/or to the widespread use of pesticides and agricultural chemicals which may contain heavy metals and other elements as active ingredients. A NHL case (n = 88) – control (n = 132) study of drinking water quality was conducted by questionnaire, and by measuring the concentrations of 64 chemical elements simultaneously in drinking water samples utilizing inductively coupled plasma mass spectrometry technology. These data are reported either as geometric means or log-transformed because of skewness. Independent two sample t-test was used to compare the concentrations of each chemical element in samples of drinking water obtained from the homes of cases and controls. The chemical elemental analysis revealed statistically significant case/control differences in concentrations of 15 chemical elements versus three expected based on chance. Elements for which the concentrations in case/control drinking water samples differed were: lithium, boron, aluminum, silicon, phosphorus, titanium, cobalt, arsenic, selenium, yttrium, zirconium, cadmium, cesium, gadolinium, and uranium. The mean aluminum concentration was higher in water taken from the homes of controls compared to case homes, while the reverse occurred for the other 14 elements, with statistically significant differences in concentration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".