Community-Based Research on H.pylori Infection in the Canadian Arctic: Findings Show a High Prevalence of Severe Gastritis.
Bibliographic record
Abstract
INTRODUCTION: Residents of Arctic Aboriginal communities have a high prevalence of H.pylori infection. In response to community concerns about cancer risk from this infection, the CAN Help Working Group established community projects to investigate the disease burden and improve disease control strategies. We present preliminary analysis of the distribution and determinants of gastritis severity in Aklavik, Northwest Territories and Old Crow, Yukon, where community-wide screening estimated H.pylori prevalence at 58% (192/332) and 68% (126/186), respectively. METHODS: Consenting participants underwent upper endoscopy with gastric biopsy in 2008 in Aklavik and 2011 in Old Crow. For each participant, 5 biopsies were assessed for H pylori and gastritis severity by a single pathologist using the Sydney classification. Data on potential risk factors came from structured interviews. ORs and 95% CIs for the effect of exposures of interest on severe gastritis among H.pylori -positive participants were estimated by logistic regression. RESULTS: In both communities, < 10% of H.pylori –positive persons had normal-mild gastritis, so gastritis severity was dichotomized as severe v. mild/moderate. The prevalence of severe gastritis was high (Aklavik, 43%, CI 34%,52%; Old Crow, 65%, CI 51%,77%). As a potential risk factor of community interest, initial analysis focused on the effect of untreated river water consumption on severe gastritis prevalence; the estimated OR was 1.8 (CI 0.86, 3.8) adjusting for age, ethnicity, educational attainment, alcohol consumption, smoking, NSAID use and community. Given the potential for variation in water quality by community, this effect was also estimated by community (Aklavik, 2.8,CI 1.1,7.2; Old Crow 0.85,CI 0.19,3.9).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".