Seroprevalence and Risk Factors for <i>Helicobacter pylori</i> Infection in Greenlanders
Bibliographic record
Abstract
BACKGROUND: In contrast to most populations worldwide, the incidence of gastric cancer increases among Inuit in Greenland. Contributing factors to this increase are unknown, but Helicobacter pylori may be involved. However, little is known regarding the epidemiology of H. pylori in Arctic communities. With the aim of determining age-specific prevalence, risk factors, and association with clinical conditions of H. pylori infection, we carried out a population-based study of H. pylori in Sisimiut, the second biggest town of Greenland. MATERIALS AND METHODS: A population-based sample of 685 persons had serum drawn that was analyzed for H. pylori IgG antibodies using enzyme-linked immunosorbent assay (ELISA). Risk factors analyses were carried out using multivariate logistic regression models. RESULTS: The seroprevalence was lowest among children aged 0-4 years (6%), but increased rapidly thereafter. In persons aged 15-87 years the seroprevalence had stabilized around 58%. Total number of children in household, number of older, but not younger, siblings and narrow age gap to closest older sibling were associated with H. pylori seropositivity. In contrast, number of adults in household and socioeconomic status did not influence serostatus. CONCLUSIONS: The age-specific prevalence pattern in Greenland is intermediate between that of developing and developed countries. The risk factor pattern indicates crowding and older siblings in particular to be key elements in risk of infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".