The Prevalence of <i>Helicobacter pylori</i> Infection in a Quaternary Hospital in Canada
Bibliographic record
Abstract
Background: Helicobacter pylori ( H. pylori ) prevalence in Canada has been estimated to be around 20% to 30%. However, H. pylori prevalence is declining in industrialized countries. We conducted a retrospective study on a population of patients referred for esophagogastroduodenoscopy (EGD) in a Canadian quaternary hospital to see the current prevalence of H. pylori infection and identify its main risk factors. Methods: We performed a retrospective cross-sectional study from the Electronic Medical Records of 500 patients who visited our endoscopy clinic and who had biopsies to search for H. pylori infection. In addition to the outcome of the biopsies, we collected demographic characteristics of patients, EGD indication and endoscopic findings. Results: The overall prevalence of H. pylori was 13.0% (65/500) among our population. We found no association with age, sex, tobacco or alcohol consumption. However, we noticed a significantly higher prevalence of H. pylori among African (25.0%; 8/32), Asian (30.8%; 4/13) and South American (34.9%; 15/43) born subjects when compared to the Caucasian group (8.0%; 28/350) (all P < 0.05). Conclusions: The prevalence of H. pylori in Canada is declining, particularly among its Caucasian population. The race seems to be the strongest risk factor associated with this infection. J Clin Med Res. 2020;12(11):687-692 doi: https://doi.org/10.14740/jocmr4348
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".