Increasing our knowledge about the epidemiology of <i>Helicobacter pylori</i> in Nunavik’s Inuit population (Québec, Canada) using <i>Qanuilirpitaa?</i> 2017 cross-sectional survey
Bibliographic record
Abstract
Helicobacter pylori is a bacterium that may colonise and proliferate in human stomachs, leading invariably to chronic inflammation and, to a lesser extent, to peptic ulcers and cancer. The main objective of this study is to describe the epidemiology surrounding H. pylori in Nunavik’s Inuit population using the 2004 and 2017 Health Surveys. Estimated prevalences were 70.9% for bacterial colonisation using a stool antigens test (SAT), 72.5% for anti-H. pylori antibodies, 12.7% for faecal occult blood in participants aged ≥ 50 and respectively of 28.4%, 11.2% and 2.4% for a prior diagnosis of colonisation, gastritis and peptic ulcer in the medical charts, with under five cases of gastric cancer reported. Variables associated with higher SAT+ prevalence were the number of household members (prevalence ratio [PR] = 1.03) and age (quadratic relationship), whereas mainly drinking municipal (PR = 0.84) and natural water (PR = 0.72) compared to bottled water, and increasing alcohol consumption (PR = 0.96) were associated with reduced prevalence. Despite current regional guidelines targeting high risk individuals in the context of high prevalence, Nunavik’s health authorities must remain vigilant by following gastric cancer incidence and the rapid evolution of guidelines, while considering local realities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".