Community-driven research on environmental sources of<i>H. pylori</i>infection in arctic Canada
Bibliographic record
Abstract
The role of environmental reservoirs in H. pylori transmission remains uncertain due to technical difficulties in detecting living organisms in sources outside the stomach. Residents of some Canadian Arctic communities worry that contamination of the natural environment is responsible for the high prevalence of H. pylori infection in the region. This analysis aims to estimate associations between exposure to potential environmental sources of biological contamination and prevalence of H. pylori infection in Arctic Canada. Using data from 3 community-driven H. pylori projects in the Northwest and Yukon Territories, we estimated effects of environmental exposures on H. pylori prevalence, using odds ratios (OR) and 95% confidence intervals (CI) from multilevel logistic regression models to adjust for household and community effects. Investigated exposures include: untreated drinking water; livestock; dogs; cats; mice or mouse droppings in the home; cleaning fish or game. Our analysis did not identify environmental exposures associated clearly with increased H. pylori prevalence, except any exposure to mice or mouse droppings (OR = 4.6, CI = 1.2-18), reported by 11% of participants. Our multilevel models showed H. pylori clustering within households, but environmental exposures accounted for little of this clustering; instead, much of it was accounted for by household composition (especially: having infected household members; number of children). Like the scientific literature on this topic, our results do not clearly implicate or rule out environmental reservoirs of H. pylori; thus, the topic remains a priority for future research. Meanwhile, H. pylori prevention research should seek strategies for reducing direct transmission from person to person.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".