1447Gastric histopathology by Helicobacter pylori cagA status in Arctic Canada
Bibliographic record
Abstract
Abstract Background Our community-driven projects address concerns of Canadian Arctic Indigenous communities about Helicobacter pylori (Hp) infection, responsible for elevated gastric cancer mortality in the region. Community research partners wished to learn whether bacterial characteristics determine severity of Hp-related disease in their communities. We aimed to describe gastric histopathology by cagA genotype of Hp isolated from residents of 7 Indigenous communities in the Northwest Territories and Yukon. Methods Participants underwent gastroscopy with 5-6 biopsies taken for histopathological assessment and 2 biopsies taken for tissue culture during 2008-2017. We used multiple PCR reactions and DNA sequence analysis to classify Hp genotypes as cagA+ or cagA-. A single pathologist used the updated Sydney classification system to grade severity of 5 gastric pathology outcomes: Hp density; chronic gastritis; active gastritis; atrophy; and intestinal metaplasia. We estimated prevalence of each outcome with 95% confidence intervals (CI) by gastric subsite and cagA status. Results Of 262 Hp isolates assessed, 142 (54%) were cagA+. Prevalence of moderate-high Hp density, severe chronic gastritis, moderate-severe active gastritis, atrophy, and metaplasia were (%[CI]): respectively, 78[70-85], 44[36-53], 65[56-72], 55[46-63], 25[18-33] in cagA+ participants and 61[52-70], 35[27-44], 31[23-40], 32[23-41], 8[4-15] in cagA- participants. cagA+ participants had higher prevalence of all outcomes in antrum and corpus. Conclusion Hp-infected Indigenous residents of Arctic Canada who harbored cagA-positive strains had higher prevalence of more severe gastric pathology than those with cagA-negative strains. Key messages Community-driven research answers questions posed by those who bear the disease burden.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".