Correlates of infection with<i>Helicobacter pylori</i>positive and negative cytotoxin-associated gene A phenotypes among Arab and Jewish residents of Jerusalem
Bibliographic record
Abstract
We examined the prevalence and correlates of Helicobacter pylori (H. pylori) infection according to cytotoxin-associated gene A (CagA) phenotype, a main virulence antigen, among the ethnically diverse population groups of Jerusalem. A cross-sectional study was undertaken in Arab (N = 959) and Jewish (N = 692) adults, randomly selected from Israel's national population registry in age-sex and population strata. Sera were tested for H. pylori immunoglobulin G (IgG) antibodies. Positive samples were tested for virulence IgG antibodies to recombinant CagA protein, by enzyme-linked immunosorbent assay. Multinomial regression models were fitted to examine associations of sociodemographic factors with H. pylori phenotypes. H. pylori IgG antibody sero-prevalence was 83.3% (95% confidence interval (CI) 80.0%-85.5%) and 61.4% (95% CI 57.7%-65.0%) among Arabs and Jews, respectively. Among H. pylori positives, the respective CagA IgG antibody sero-positivity was 42.3% (95% CI 38.9%-45.8%) and 32.5% (95% CI 28.2%-37.1%). Among Jews, being born in the Former Soviet Union, the Middle East and North Africa, vs. Israel and the Americas, was positively associated with CagA sero-positivity. In both populations, sibship size was positively associated with both CagA positive and negative phenotypes; and education was inversely associated. In conclusion, CagA positive and negative infection had similar correlates, suggesting shared sources of these two H. pylori phenotypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".